Large language models have already transformed software engineering. Now, large physics models are also starting to transform design engineering.
Geometry generation is getting better on the AI platform.
This blog explores the similarities and differences between Machine Learning Engineers and AI Engineers.
In this new APDRC white paper, learn how Neural Concept is leveraging AI and advanced NVIDIA RTX GPU compute to accelerate simulation.
Deep Learning exploits past FEA analyses and associated CAD geometries to produce predictions accessible to all engineers, not just specialists, for addressing stress concentration, such as in the design and optimization of turbo machinery.
Unlock the Power of Generative AI in Design with Deep Learning.
This article discusses automation's past and future impact on mechanical engineering with applications reshaping the industry. We also present AI in mechanical engineering and discuss the career prospects for students who seek employment by specializing in these aspects of the industry.
Multi-objective optimization of a car's side view mirror design. Watch the video.
AI is accelerating, and OEMs are racing to keep pace by integrating 3D AI into their CAE workflows.
LS Electric has published a technical paper demonstrating how Neural Concept is enhancing engineering efficiency by offering superior prediction accuracy for Mold Transformer Electrical Properties compared with existing design formulas.
Automakers are using AI to speed up development times for next-generation vehicles.
How is Artificial Intelligence (AI) and, more specifically, Machine Learning revolutionizing engineering processes in mechanical engineering?
Neural Concept announces that its 3D AI platform will leverage the NVIDIA Omniverse Blueprint.
The article shows how AI is transforming engineering by enabling teams to analyze, predict, and optimize systems. ML models enhance design and decision-making while adhering to ethical standards.
The technical paper underscores the potential of Neural Concept’s technology to accelerate AI for engineering by offering faster, more accurate, and AI-driven engineering results.
While AI offers powerful capabilities, it is not a magic solution. Successfully applying AI in manufacturing requires a deep understanding of the technology and the manufacturing process it's being applied to.
This paper underscores the potential to accelerate AI for engineering by offering faster, more accurate, and AI-driven engineering results. Results include a 30% improvement in pressure drop and 10% weight reduction.
This paper that explores use of convergence rates as a proxy for uncertainty estimation in deep learning networks.
This webinar features real-life examples customer successes and use cases, from thermal management and external aerodynamics models.
Reduced-order modelling traditionally requires parametrizing CAD geometries, while CAD-embedded simulations usually require downgrading in the CAE fidelity and introduces uncertainties.
Graph Neural Networks (GNNs) can predict the performance of a shape quickly and accurately and be used to optimize more effectively than traditional techniques that rely on response-surfaces obtained by Kriging.
This article introduces the dynamic analysis process and characterizes some engineers' structural dynamics applications.
All industries have safety issues, but aerospace and defense are particularly sensitive. One solution is Neural Concept's AI tool, where the CAD and CAE data can entirely reside within the company to ensure the safety of proprietary and sensitive data.
Learn how machine learning in predictive maintenance can offer benefits such as cost savings, increased reliability, extended equipment life, and customer satisfaction.
In this video, you can see how the user is able to navigate on the performance map, evaluating the behavior of the design on specific operating conditions, for different values and views (pressure field, velocity field, etc.). Then, the user can upload a new geometry, and get the instantaneous predictions of the model on the whole range of operating conditions.
This article deals with basic concepts of structural mechanics, methods of analysis, and applications across engineering disciplines, with a particular emphasis on the computer simulation technique known as Finite Element Analysis (FEA) or Finite Element Method (FEM).
This article explores simulation-driven design and the benefits it offers to engineers who are at the forefront of designing and optimizing products and processes for their organizations.
This article reviews how design departments can approach their workflows effectively with NURBS vs mesh modeling.
This article discusses how the study of stress concentration ensures the structural integrity and reliability of engineered components. It also explores how simulation techniques ranging from FEA to deep learning applications can be implemented to assist iterative engineering design.
Can AI help structural engineers?
This static analysis introduction focuses on the resilience of simple structural elements like roof beams or bridges and closes with an explanation of why arches stand.
Why Manufacturing Simulation? From Inventory Control to Lean Manufacturing.
Within the automotive digital thread, the AI solution, as complementary to the consolidated FEA approach, promises to revolutionize further how cars are designed and tested for safety while reducing development costs.
Training simulation AI takes a lot of computational resources; Rescale has a solution.
What is Response Surface Methodology?
This paper reports the performances of NCS on the problem of thermal simulations for satellite panel and discusses the potential impact on the design workflow.
This webinar recording features experts from Microsoft, Altair, and Neural Concept. See practical, real-world examples of the use of AI and Machine Learning to improve the simulation process.
Neural Concept Director of Operations, Thomas von Tschammer and Łukasz Miroslaw, Senior HPC/AI Specialist, Microsoft join CIMdata’s Sandeepak Natu to share their thoughts on AI in cutting-edge simulation.
Generative Design starts with counterintuitive design alternatives. It leads to state-of-the-art products that defy conventional design and manufacturing methods.
Thanks to knowledge transfer across different categories of design with only a few samples, NCS makes introducing significant changes possible while leveraging existing simulation data.
This blog discusses the "classic" CFD applications, and the innovations introduced by data-driven simulation.
An interview with Pierre Baqué, founder and CEO.
In this webinar, engineering experts Thomas von Tschammer (Neural Concept) and Anthony Massobrio (Intelligent Simulation) present a practical Automotive Use-Case.
This document contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations.
There are some important steps to exploit the full potential of 3D Deep Learning (but also of any machine learning technique): preparing your data so that the model can extract the maximum of information out of it.
This article provides a high-level overview of multi-objective global function optimization and the benefits one can unlock utilizing deep learning approaches in constructing the surrogate model used in the optimization process.
A revolutionary approach is transforming the way companies approach tough engineering optimization challenges.
DE chats with Malcolm Panthaki, co-founder of Revolution in Simulation™, about their mission to expand the reach of simulation technology.
With Neural Concept Shape, you can use 3D numerical simulations as input to train your deep learning models. If we take the example of aerodynamic simulations, these CFD simulations results are usually much larger files than images or text (a single result can reach several hundreds of GB). Hence storing a large amount of them can become an issue in the long term, as it would require to regularly scale up the hardware infrastructures accordingly.
A deep learning solution can assist engineers from first iteration to final configuration without the hassle to learn computer science, data science or machine learning thanks to bespoke interfaces sitting on a background solution managed by experts.
The automotive industry faces challenges; design teams in automotive companies are under pressure. Can artificial intelligence (AI) help product design teams in the automotive industry? How can supply chains become more efficient with AI and data science?
Geometry generation is getting better on the AI platform. Plus, Siemens launches Digital Twin Composer and more design and simulation … Continue reading Neural Concept launches AI Design Copilot at CES 2026
Neural Concept, the leading end-to-end 3D AI platform for engineering intelligence, today announced it is using the NVIDIA Omniverse Blueprint for … Continue reading CES 2025: Neural Concept 3D AI platform accelerates product innovation with NVIDIA Omniverse Blueprint
Neural Concept, the leading end-to-end 3D AI platform that transforms product development and design with Engineering Intelligence, will be exhibiting … Continue reading Neural Concept to Demonstrate Accelerated AI-Driven Product Design at CES 2025
Lausanne, Vaud, Switzerland, 27 June 2024 // Neural Concept, has today announced a collaboration with Siemens Digital Industries Software to integrate Simcenter™ STAR-CCM+™ … Continue reading Neural Concept collaborates with Siemens to enable OEMs to make faster decisions with rapid results prediction
Neural Concept, a world-leading pioneer in 3D deep learning technology for product design, today announced a new partnership with Rescale, a … Continue reading New Era of “Engineering Intelligence” As Neural Concepts Partners with Rescale to Deliver 3D AI with HPC SCALE
CINCINNATI, OH (USA); FEBRUARY 21, 2023: The global simulation industry collaboration and technology alliance Revolution in Simulation™ (“Rev-Sim” at www.rev-sim.org), … Continue reading Revolution in Simulation™ Welcomes Neural Concept as Newest Sponsor
Thanks to this innovation by Neural Concept, Subaru will bring the next generation of vehicles to market more efficiently by 2028.
Simulation has become an invaluable tool for meeting the complex challenges of electric vehicle development.
Danfoss is currently collaborating with Neural Concept to evaluate the integration of Neural Concept Shape (NCS) deep learning technology within their product design workflows.
Neural Concept Shape models were able to accurately predict the behavior of components during a crash, a highly non-linear phenomenon.
PhysicsX, the team of scientists and engineers borne out of Formula One, collaborated with Neural Concept in order to build a surrogate model allowing to predict in real-time the performance of various heat exchanger designs, with different topologies.
To meet the ever-evolving demands of the industry and customers Mitsubishi Chemical Group is committed to support their partners with material characterizations used as input data for highly dynamic crash simulations using Finite Elements Analysis.
The drive towards full electrification of the car is an opportunity but also a challenge. Certain elements of the drivetrain will disappear, such as exhaust systems. On their turn, components such as the battery are now becoming prevalent.
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