La entrada From simulation engineers to AI-builders: the future of industrial simulation se publicó primero en MIURA SIMULATION.
]]>AI in simulation: a new layer for engineering expertise
For decades, simulation engineers have been at the heart of industrial innovation. They translate physics into models, explore design options, and help companies make critical decisions long before anything is built.
Today, artificial intelligenceis entering this landscape. Not as a replacement for simulation, but as a new layer that can amplify engineering expertise. Yet adopting AI is not simply a matter of adding algorithms. It requires a shift in how engineers work with data.
Why AI should belong to engineers
In many organizations, models are built by specialists, trained on data that engineers may not fully control, and deployed as black boxes. This approach creates friction. Simulation engineers struggle to trust results they cannot explain, adapt or improve. Over time, AI becomes disconnected from real design constraints.
We believe the opposite approach is needed.
AI should be built by simulation engineers, using their own simulation data, their own assumptions, and their own understanding of the systems they design.
The emergence of the AI-builder
An AI-builder is not a data scientist stepping into an engineer’s role. It is an engineer who can design, train and refine AI models as a natural extension of their simulation work.
This evolution does not require engineers to become AI specialists. What it requires is the right foundation: tools and infrastructure that make AI accessible, reliable and consistent with engineering reality. When AI fits naturally into existing workflows, engineers can focus on what they do best: understanding systems, making trade-offs, and validating results.
For this to work, simulation data must be usable by design. It needs to be structured and qualified, reusable across projects, and accessible without long, manual preparation phases. Without these conditions, AI remains an attractive idea but a fragile reality. The gap between potential and practice simply becomes too wide.
Data as the missing link
Over time, simulation produces a wealth of knowledge. Each model, each result, each iteration captures decisions and assumptions that reflect real engineering expertise. Yet when this data is fragmented or locked inside tools, much of this knowledge becomes difficult to reuse.
When simulation data is organized and made ready for AI, its value changes. Engineers can build on past work instead of starting from scratch. They can compare designs more efficiently, identify patterns across projects, and train AI models that reflect their specific domain knowledge rather than generic assumptions.
In this context, AI is no longer an external system layered on top of engineering workflows. It becomes an extension of engineering judgment, shaped by experience, constraints and real-world understanding.
A change in responsibility and ownership
Turning simulation engineers into AI-builders also reshapes responsibility.
Engineers remain directly accountable for how models are built, which data is used, and how results are interpreted. This continuity is essential. It preserves trust in results, transparency in decision-making, and control over intellectual property.
Rather than shifting ownership away from engineering teams, AI stays in their hands. The teams who understand the systems best remain responsible for how AI is created and applied. This is what allows AI to scale in industry without becoming a black box—and what makes the role of the AI-builder both credible and sustainable.
Building the future of industrial simulation
As industrial systems continue to grow in complexity, the role of simulation engineers will keep evolving. AI will become a natural part of their toolkit, not a separate discipline.
At its core, this transition is about making better use of existing data and giving engineers the tools to build AI that reflects how they actually work.
This is where we see industrial simulation going. To support this direction, we recently completed a funding round, allowing us to move faster in building the foundations simulation engineers need to create, own and evolve their AI models.
Learn more about our funding round
Press release (english version)
| Miura Simulation raises €2M to democratize the use of artificial intelligence in industrial simulation
Communiqué de presse (version française)
| Miura Simulation lève 2 M€ pour démocratiser l’usage de l’intelligence artificielle dans la simulation industrielle (LinkedIn 
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La entrada From simulation engineers to AI-builders: the future of industrial simulation se publicó primero en MIURA SIMULATION.
]]>La entrada Miura Simulation raises €2M to democratize the use of artificial intelligence in industrial simulation se publicó primero en MIURA SIMULATION.
]]>Press release (english version)
| Lisez le communiqué en français (LinkedIn).
Miura Simulation, a DeepTech startup specializing in accelerating industrial simulation through AI, announces its first funding round of €2M.
The operation brings together a group of investors including Atlantique Business Angels Booster, Pays de la Loire Participations, Centrale Innovation, Arts et Métiers Business Angels, Aerospace Angels, with the banking support of Bpifrance, Crédit Agricole, Caisse d’Épargne, and Crédit Mutuel.
This funding will accelerate the commercial deployment of the solution and position Miura as a leading European player enabling simulation engineers to independently design their own AI models.
Data: a major barrier to AI adoption in industrial simulation
In aerospace, automotive, or energy, simulating product behavior (aerodynamics, acoustics, safety) still requires hours or even days of computation.
While engineers must design increasingly complex products, under tighter deadlines and growing regulatory pressure, current computation time has become a critical bottleneck.
Although AI enables simulations in a matter of seconds, its adoption is hampered by a well-known challenge for industrial companies: data.
Contrary to the common belief that computation is the main cost driver, it is the human effort required to generate, transform, prepare, qualify, and operationalize data that causes AI budgets to soar. This challenge is amplified by dispersed, heterogeneous, and difficult-to-use datasets.
Miura Nexus: industrializing data exploitation
To remove this barrier, Miura Simulation developed Miura Nexus, a solution that transforms simulation data into an asset ready for AI.
Nexus centralizes data, simplifies preparation, and eliminates obstacles linked to heterogeneous formats, data reliability, and software silos.
Benefits for industrial companies:
- Reduction by 70 to 90% of human effort related to data processing
- Faster design cycles
- Lower AI adoption costs
- Protection of intellectual property
By regaining control of their data, engineers can design expert AI models perfectly tailored to their domain. Data then becomes a unique and sustainable competitive advantage.
“Thanks to the support of our investors and industrial partners, we are moving from a proven technology to a product deployed in the field.
Our ambition is clear: remove the major barrier to AI adoption in simulation (the lack of structured and qualified data) and help industry enter the era of AI builders.”
— Jose Aguado, CEO & co-founder, Miura Simulation
They support Miura Simulation
“We are honored and proud to support Miura Simulation at a pivotal moment in its development.
The industrial challenges that Miura Nexus aims to address are fascinating, and the potential use cases of AI-driven innovation are virtually unlimited.
We have full confidence in the Miura Simulation team to demonstrate the relevance of its technology and showcase DeepTech made in Nantes.”
— Fabien Dufrênet, member of ABAB and Miura Simulation lead investor
“The investment of Pays de la Loire Participations (PLP) in Miura Simulation is fully aligned with our strategy focused on high-potential innovation and breakthrough technologies.
The project, which places data quality at the heart of AI, provides industrial companies with significant productivity gains in their simulation activities. This first funding round is a key step to support future developments. As proof of this momentum, Miura has already convinced several major industrial players across aerospace, space, and automotive sectors.”
— Anne Blanche, Managing Director of PLP
About Miura Simulation
Miura Simulation is a French DeepTech company born at École Centrale de Nantes, driven by the belief that AI can profoundly transform the design of industrial products.
Founded in 2020 by Jose Aguado, Domenico Borzacchiello and Jordi Gómez, Miura relies on a founding team combining scientific expertise and business vision to accelerate industrial AI adoption.
Today, this Nantes-based startup supports leading industrial players in aerospace, energy, and automotive to deploy AI at the core of simulation and speed up design cycles.
Miura brings together a team of 9 talents, including 5 PhDs, combining scientific excellence and industrial execution.
Miura’s mission: give engineers full control over their data so they can become “AI builder” and build a sustainable competitive advantage.
Press contact
Email: [email protected]
Website: www.miurasimulation.com
They talk about us
La entrada Miura Simulation raises €2M to democratize the use of artificial intelligence in industrial simulation se publicó primero en MIURA SIMULATION.
]]>Today, the pace of innovation has accelerated. To keep up, the automotive industry now relies extensively on digital simulations. These computer-based models allow engineers to test ideas virtually before building physical prototypes, saving time and money while making the design process more efficient and reliable.
Despite these advances, some simulations, such as computational fluid dynamics or crash testing, still require a lot of computing power and time. In particular, crash simulations are critical, as they directly impact vehicle safety. Improving both their runtime and accuracy remains a key challenge.
La entrada Use of scarce crash simulation data to build efficient surrogate models se publicó primero en MIURA SIMULATION.
]]>From crash simulations to surrogate models
Even though digital simulations are widespread, each run — whether for crash testing or airflow analysis — is costly and limited in number. Strict confidentiality between car manufacturers also means valuable data is often locked away in silos, preventing reuse and collaboration.
To address these constraints, it is essential to maximize the value of existing simulations. One promising approach is the use of surrogate models: fast, lightweight stand-ins that estimate the behavior of high-fidelity simulations using previously generated data. These models can significantly reduce the time and resources required to explore new design configurations.
This article sums up the work presented at SIA Simulation Numérique 2025 [1]. It compares two surrogate modeling methods applied to crash test data from a benchmark provided by SIA, Renault and Stellantis [2].
- ReCUR: a reduced-order modeling technique that builds compact, fast, and accurate surrogate models from existing high-fidelity data. It is non-intrusive –no direct access to finite element solvers is needed — making it easy to integrate into engineering workflows.[3]
- Neural Fields: an advanced ML approach that also learns from existing simulations to create fast, accurate estimations.[4]
Both approaches use the same inputs and produce comparable outputs, allowing for a fair comparison.
We will also explore how transforming and preparing data properly can significantly boost model accuracy. This underlines a key insight: the engineer’s expertise and understanding of their data are just as important as the surrogate modeling method itself.
This work follows earlier efforts advocating a data-focused mindset in industrial companies, as outlined in our article: Overcoming key barriers to AI adoption in engineering simulation
Simulation data should indeed be recognized as a valuable asset rather than byproduct. Effectively managing this data enables the training of machine learning models that become increasingly powerful as data volumes grow, while still delivering meaningful insights even in low-data scenarios.
The crash simulation database
The dataset consists of 60 crash simulations of a structure representing a vehicle’s front. In these tests, variations are created by changing the thickness of six structural crash boxes.
— Example simulation of the structural dynamics
By virtually testing many different configurations, engineers can identify the designs offering the best trade-off between crash protection and structural weight. When automated, this process becomes an optimization study, where the system intelligently explores a wide range of possible designs to find the one that performs best based on safety and other key criteria. Virtual testing not only accelerates development but also ensures that every decision is backed by solid data, helping engineers design safer vehicles.
Exploring the parametric domain
The 60 parametric configurations are displayed on the following scatter plot:
— Scatter plot of the parametric domain (each point represents a configuration).
The domain is well covered, with no strong correlations between input parameters. Miura’s tools make it easy to analyze such domains efficiently and, if needed, to explore specific solutions in greater detail using visualization solutions like ParaView.
In that database, the number of simulations available is ten times the number of design parameters, a common benchmark for optimization studies. One of the key challenges is to build a surrogate model with only a limited number of high-fidelity simulations. Then we propose to evaluate the accuracy according to the number of training simulations used, in order to assess whether the surrogate is reliable enough to support full optimization workflows.
From simulation to smart design: a streamlined workflow
The workflow starts with data transformation: turning complex simulation outputs into a format suitable for machine learning.
We then train fast, surrogate models using tools like Keras and TensorFlow.
Once trained, the models can be deployed in real-time through a ParaView plug-in, enabling engineers to interact directly with predictions, explore design alternatives, and even couple the model with optimization algorithms to automatically search for the best design based on key performance indicators (KPIs) like safety or efficiency.
Miura Nexus platform connects and automates these steps. It unifies data across high-fidelity solvers, overcomes the limitations of proprietary formats, and applies observability principles to give teams full ownership of their simulation data. This foundation simplifies workflows and paves the way for adoption of scalable AI in engineering.
The substitution models
Two surrogate models were trained, one with the ReCUR reduced order modeling technique and another using neural field approach. Both were designed to minimize the error between estimations and high-fidelity reference data.
- ReCUR model: built on a reduced-order basis from CUR decomposition, with regression handled by a neural network. In this method, predictions are constrained to the space given by the bases directly built using high-fidelity data.
- Neural field model: directly maps configuration parameters (thicknesses), time steps and mesh nodes to displacement outputs.
— Principle schema of the ReCUR method
— Typical architecture of a Neural Field Network
Smarter modeling through expert-driven data transformation
Initial results with a single Neural Field were unsatisfactory, especially in areas of large deformation or rigid-body motion. Such areas are highlighted in the red circles on the subsequent figure.
— Comparison between high-fidelity results and one single Neural Field estimates.
To improve accuracy, the displacement data was split into two components: one global that captures the overall movement of the structure (called rigid body motion), and another local that captures the local deformations during impact.
We then trained two separate neural field models, one for each component. This split allows each model to focus on a specific scale: global for rigid body motion, and local for deformation. The result is a much more accurate and reliable surrogate model.
This approach highlights how critical data transformation is in building effective machine learning models. It also shows the value of involving human experts in the process — because understanding the physics behind the data is key to make smart choices that machines alone can’t infer. Keeping humans “in the loop” leads to better, faster, and more trustworthy models.
— Traning strategy for the two steps neural field
At Miura, we developed Nexus to make the implementation, testing, and deployment of data transformations seamless and efficient. Built on a ‘pipeline-as-code’ philosophy, Nexus enables rapid prototyping on local datasets and scalable deployment across entire simulation datasets. In doing so, it bridges the gap between the worlds of simulation and machine learning.
Be among the first to explore Nexus, test new capabilities, and shape its roadmap.
Join our Pioneers Program
Results
The subsequent figure compares the neural field approach without data transformation (on the left) and with the data transformation (on the right). The high-fidelity results are represented in light grey. This comparison demonstrates the improvements in the large deformation region and for the rigid body parts, indicating that the surrogate model better captures the physical behavior of the structure.
— Comparison of the estimates using single neural field (on the left) and two steps approach (on the right). High-fidelity displayed in light grey.
The estimation from the ReCUR model is shown below. The accuracy is satisfactory although some remain, notably in the red-circled region where a discontinuity in the mesh appears. Note that the data transformation was not applied to that surrogate model.
The two approaches are compared by computing the global error between the estimation results and the high-fidelity simulation. This error is tracked across all computations relative to the database size.
It shows that the ReCUR model performs better when the number of training computations is low, while the neural field becomes more precise when the training set contains more than 20 computations.
The ReCUR model trained on 40 computations required in 5 hours, while the neural field model took 15 hours. The estimation time for the ReCUR model is about 10 seconds, compared to under one second for the neural field model.
Note that optimization tasks can be effectively performed using the surrogate model built with only 20 high-fidelity simulations. The error remains acceptable for this type of study, where obtaining a reliable ranking of technical solutions is more critical than achieving highly precise estimations. In contrast, solving the same optimization problem directly with the high-fidelity solver, even with an efficient optimization algorithm, would require approximately 60 high-fidelity simulations. Using a surrogate model trained on just 20 high-fidelity simulations therefore results in significant time savings.
Interactive use
Beyond optimization, surrogate models can also be embedded in interactive plug-ins. Engineers can interact with the models as they would with traditional high-fidelity simulations. They can visualize estimations for unexplored configurations on the fly and perform dedicated post-processing to extract KPIs. This interactivity tool makes surrogate models powerful tools for narrowing down promising design directions.
The video below demonstrates the use of the model in an interactive plug-in.
Conclusions
This work demonstrates that efficient surrogate models can be built from a limited number of crash simulations.
A key takeaway is the importance of transforming the data in a way that reflects the physical behavior –a step that dramatically improves model accuracy. It also reinforces a central principle: human expertise remains essential. By embedding domain knowledge into data preparation, engineers can guide machine learning tools toward better, faster, and more trustworthy results.
Machine learning techniques, supported by Miura’s Nexus platform, offer industrial companies a way to take control and ownership of their simulation data. This capability is already proving valuable not only in automotive crash testing but also in aerospace, as demonstrated in our collaboration with CNES on antenna placement optimization.
Read our articles about how the collaboration with CNES is a step forward to integrate AI simulation in the space industry.
These examples show the versatility of the technology and its ability to support engineers across diverse industrial sectors.
Looking forward, this work opens the door to geometric deep learning—a powerful approach that can handle not just parametric variations, but also geometric and structural changes. This will be especially valuable in early-stage vehicle development, where exploring many design concepts quickly is critical.
[1] : T. Defoort, Y. Le Guennec, J. V. Aguado, D. Borzacchiello. Comparing traditional surrogate modelling and neural fields for vehicle crash simulation data. SIA Simulation numérique 2025, SIA, Apr 2025, Guyancourt (78), France.
https://hal.science/hal-05097364/
[2] : SIA Simulation numérique : challenge réduction de modèle :
https://www.sia.fr/evenements/371-challenge-reduction-modele?lng=en
[3] : Y. Le Guennec, J. P. Brunet, F. Z. Daim, M. Chau, Y. Tourbier: A parametric and non-intrusive reduced order model of car crash simulation. Computer Methods in Applied Mechanics and Engineering, 338, 2018.
https://hal.science/hal-01485276/document
[4] : Y. Xie et al.: Neural Fields in Visual Computing and Beyond, Computer Graphics Forum, 41, 2, 2022.
https://arxiv.org/abs/2111.11426
La entrada Use of scarce crash simulation data to build efficient surrogate models se publicó primero en MIURA SIMULATION.
]]>La entrada How the collaboration with CNES is a step forward to integrate AI simulation in the space industry se publicó primero en MIURA SIMULATION.
]]>Rethinking antenna placement: a complex puzzle
In space engineering, every design decision can have major consequences. One of the most complex challenges is antenna placement. This process requires balancing multiple constraints: ensuring optimal signal performance, reducing weight, and integrating components seamlessly into a satellite or aircraft structure. Yet, traditional methods rely on lengthy simulations, slowing down the innovation process.
To address this challenge, we collaborated with the Centre National d’Études Spatiales (CNES) to explore an innovative approach: AI simulation.
Accelerating design cycles with AI simulation
The promise of AI simulation is to overcome the inefficiencies caused by slow simulation cycles. In concurrent engineering, where multiple teams work simultaneously, delays in simulation can disrupt workflows and hinder decision-making. AI-driven simulation addresses this challenge by transforming historical simulation data into actionable models that provide results in seconds. This approach enhances efficiency, accelerates iterations, and fosters seamless collaboration.
Unlike traditional methods that depend on repetitive and time-consuming testing, AI simulation significantly accelerates the process. By exploring the design space extensively, it delivers more informed results in less time, allowing engineers to concentrate on high-value tasks while keeping pace with the demands of concurrent engineering.
Addressing the data challenge: a long-term investment
One common concern with AI simulation is the need to generate large volumes of synthetic data for model training, as historical data is often unavailable. This additional step, along with the time required to train models, can initially seem to lengthen the overall design cycle—making the promise of “faster time-to-design” appear less compelling in the short term.
However, this shift represents an investment rather than a burden. By systematically building structured and reusable datasets, companies create a foundation that enhances simulation accuracy and accelerates future iterations. Unlike traditional simulation-based workflows, where data is generated on demand and then discarded, fostering a data-driven culture enables organizations to continuously refine their AI models. This approach not only unlocks efficiency gains over time but also positions AI-powered design as a sustainable competitive advantage.
A successful Proof of Concept (PoC)
In 2024, our collaboration with CNES led to a promising evolution: a successful PoC demonstrating the transformative potential of AI simulation in electromagnetic simulation for antenna placement. By automating and optimizing simulations, we have taken a significant step toward reducing iteration cycles while improving the performance of the proposed solutions.
Breaking simulation silos: How Miura enables seamless AI integration in space engineering
Engineering teams in the space industry often struggle with fragmented simulation ecosystems, where proprietary software limits interoperability and flexibility. Most AI solutions reinforce these constraints, making their integration a challenge. Miura stands apart by offering a fully interoperable and relying on open formats and standards, designed to fit seamlessly into existing workflows. This ability to integrate effortlessly with diverse tools and platforms was a key factor in our successful collaboration with CNES, where our technology aligned ideally with their demanding processes. By ensuring organizations retain full control over their data and models while leveraging the power of AI, Miura provides a scalable and future-proof approach to engineering simulation, eliminating vendor lock-in and unlocking new possibilities for innovation.
What’s next: tackling new space challenges
Building on this initial success, we are now preparing to take on even more ambitious challenges in 2025. The goal is to expand the application of AI simulation to other complex space problems. By refining our methods and broadening our scope, we are paving the way for a new standard of efficiency and innovation.
An evolution in space engineering
AI simulation is not just an incremental improvement, it represents a paradigm shift in how space systems are designed. While its initial adoption requires investment in data management, our infrastructure ensures that companies build a growing knowledge base, continuously accelerating design cycles.
This collaboration with CNES marks a key milestone in this transformation. It demonstrates that, with an innovative approach and AI-driven technology, we can unlock a future where engineers spend less time on lengthy and repetitive processes—and more time on creativity and innovation.
La entrada How the collaboration with CNES is a step forward to integrate AI simulation in the space industry se publicó primero en MIURA SIMULATION.
]]>La entrada Overcoming key barriers to AI adoption in engineering simulation se publicó primero en MIURA SIMULATION.
]]>Why some businesses hesitate to adopt AI-based simulation
Artificial intelligence has the potential to accelerate design processes, yet many engineering and manufacturing teams remain hesitant. This reluctance often stems from cost concerns—traditional simulation-based design is already perceived as efficient. Typically, data from one simulation run is used to inform the next design iteration, then discarded once minimum requirements are met.
Simulation data is treated as a transient byproduct.
AI-driven simulation, however, requires large and carefully curated datasets. Companies that do not routinely store or organize their simulation data may see the extra effort and cost of generating comprehensive training data as outweighing the benefits. Without structured data retention, AI adoption appears more like an obstacle than an opportunity.
Barriers to AI adoption
Lack of data infrastructure
Even organizations that recognize AI’s potential often lack the infrastructure needed to capture and retain simulation data. Without scalable storage, consistent labeling practices, or centralized repositories, data remains scattered across teams and software tools. This fragmentation makes it difficult to compile high-quality datasets for machine learning. Incomplete or disorganized data provides limited value in model development and can hinder future AI initiatives.
Building a strong data infrastructure is essential. Companies can start by implementing systematic data management practices, ensuring simulation data is properly stored, labeled, and readily accessible for AI applications.
Inconsistent data formats
Most simulation software generates data in specialized or proprietary formats that do not easily integrate with machine learning applications. Converting files, aligning naming conventions, and merging results into a single dataset can be time-consuming and error-prone. These challenges add to the cost and complexity of AI adoption, especially for companies that are already uncertain about the return on investment.
Standardization is key to overcoming this challenge. By adopting universal data formats and interoperability standards, organizations can streamline AI integration and facilitate smoother workflows across different tools and teams.
Limited ownership of AI models
Many off-the-shelf AI tools offer little competitive advantage if companies cannot retrain or customize them. Third-party solutions often lock users into rigid frameworks that do not accommodate specific product requirements or manufacturing processes. To maximize value, engineering teams need the flexibility to adapt AI models to their own data and design constraints. Otherwise, they risk obtaining generic results that fail to justify the expense and effort of implementation.
The ability to modify and refine AI models based on proprietary data is what ultimately drives real innovation and efficiency. Choosing AI solutions that allow for customization ensures companies maintain control over their technological investments.
Path to AI adoption
A path forward
Rather than overhauling existing workflows, companies can take a gradual approach to AI adoption. The first step is to capture and retain more simulation data from each design iteration. Standardizing file formats and implementing reliable data management platforms lay a strong foundation for future AI projects.
At the same time, organizations can explore in-house AI solutions that allow training and fine-tuning with proprietary datasets. This ensures that AI tools remain relevant to specific business goals. By focusing on data quality, software compatibility, and model ownership, companies can realize the benefits of AI-based simulation without disrupting existing processes.
This is where Miura’s data management services come in, offering tailored solutions that enable engineering teams to transition to AI-driven workflows seamlessly.
Miura’s data management services
Pipelines-as-code
Engineering teams often use a mix of tools for simulation and machine learning, leading to inconsistent workflows. Adopting a pipelines-as-code approach ensures that every data operation—from ingestion to transformation to delivery—is defined in a structured, version-controlled environment. Instead of introducing another platform, this strategy integrates seamlessly with existing machine learning frameworks, enabling teams to manage complex data tasks with clarity and precision.
Rapid prototyping and frictionless deployment
Experimentation is crucial in advanced engineering workflows, but scaling up from small tests to full production can be challenging. Manual storage configuration and process scheduling create bottlenecks.
A fully managed service automates both aspects, allowing engineers to prototype transformations through an intuitive interface and deploy them at scale with minimal effort. Storage becomes a background process, and pipeline execution is orchestrated automatically, freeing teams to focus on optimizing their models rather than managing infrastructure.
Cross-compatibility through domain-specific data connectors
Many organizations rely on specialized simulation software, each with unique data structures and file formats. Our domain-specific connectors bridge these gaps, enabling seamless data integration across different simulation tools. Engineers can merge data into a unified pipeline without dealing with manual file conversions or inconsistent schemas. This streamlined access to diverse data sources provides a more comprehensive view of engineering processes and enables cross-domain problem-solving.
By ensuring cross-compatibility, businesses can leverage AI-driven insights across multiple simulation environments, unlocking greater innovation potential.
Self-hosted option
Security and data ownership are critical in engineering and manufacturing, especially for proprietary designs and processes. A self-hosted deployment model allows companies to run Miura’s data management framework within their own infrastructure. This ensures that sensitive information remains within the organization’s network while still benefiting from automation, scalability, and seamless integration. By maintaining full control over their data, businesses can comply with internal policies without sacrificing AI-driven innovation.
Unlock the full potential of AI in engineering simulation
By addressing the core challenges of AI adoption—data infrastructure, compatibility, and model ownership—companies can transition to AI-driven simulation with confidence. Miura’s solutions empower engineering teams to unlock the full potential of AI while maintaining flexibility, security, and control over their data.
Get started today. Contact Miura to learn how our data management solutions can streamline your AI transition and enhance your simulation workflows.
La entrada Overcoming key barriers to AI adoption in engineering simulation se publicó primero en MIURA SIMULATION.
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