This content originally appeared on DEV Community and was authored by Nick Johnson
Physics-based simulations are indispensable across engineering, aerospace, energy, and pharmaceutical sectors. They enable design validation and discovery without prototyping—but often at the cost of extensive compute power and lengthy runtimes. Traditional solvers such as finite element analysis, molecular dynamics, and computational fluid dynamics (CFD) are powerful but inherently constrained by scalability and computational cost.
With the advent of AI‑powered physics, Rescale has emerged as a leader in hybridizing high-performance computing (HPC) with machine learning to dramatically accelerate and scale simulations. Its cloud-native platform offers unprecedented agility and accessibility, enabling organizations of all sizes to harness AI‑assisted simulations with enterprise-grade governance.
Limitations of Traditional Simulation
Conventional simulation methods involve solving complex partial differential equations using discretization and iterative solvers. While accurate, they are resource-intensive and time-consuming—often requiring days per simulation for high-resolution or multi-physics models. These limitations make rapid design iteration and real‑time feedback impractical for many R&D teams.
Rescale’s platform overcomes these hurdles by injecting AI into the simulation workflow.
AI Physics on Rescale: Speed with Fidelity
Rescale’s AI Physics offering empowers users to build surrogate models that deliver simulation results within milliseconds—at speeds up to 1,000× times faster than traditional methods—while maintaining 99%+ accuracy.
The process typically involves:
Generating training datasets using classical simulation tools.
Training custom ML models (often neural networks) directly within Rescale.
Deploying trained models for real-time inference, enabling instant predictions across vast design spaces.
This workflow produces physics-based AI models that maintain interpretability and reliability, especially when combined with physics-informed neural architectures and constraint-based training. Read more about it at https://rescale.com/platform/ai-physics/.
Cloud-Scale Architecture and Workflow Automation
Rescale’s platform offers a unified control plane to run HPC and AI workloads over hybrid/multi-cloud infrastructure—without requiring on-premises investment. Customers can access more than 1,250 pre-configured CAE/R&D software applications and leverage integrations with major cloud providers such as AWS, Azure, Google Cloud, Oracle Cloud, and NVIDIA’s DGX Cloud.
The platform automates simulation pipelines from setup through inference and visualization. Integrated tools like DOE (Design of Experiments) frameworks streamline optimization and hyperparameter tuning for both simulation and AI model development.
Strategic Partnerships and Innovations
Rescale has partnered with NVIDIA, integrating their Omniverse Blueprint and Modulus framework to deliver real‑time simulation and digital twin tools in the cloud. These collaborations have enabled users to:
● Reduce preprocessing times from hours to milliseconds.
● Cut full simulation runtimes from 24 hours to mere milliseconds in some workflows.
Rescale also collaborates with Navier AI, deploying pretrained AI models for CFD that accelerate engineering simulations by 1,000× while retaining high physical fidelity.
Measured Impact from Industry Leaders
According to Rescale’s clients:
● GM Motorsports accelerated aerodynamic design and testing by deploying AI‑powered modeling on Rescale, gaining speed without sacrificing precision.
● Intelligent Energy reported reducing simulation and preprocessing time from 24 hours to milliseconds via the NVIDIA‑enabled digital twin solution on Rescale.
● Testimonials from multiple clients highlight up to 85% improved compute efficiency and 75% cost reductions per simulation job, with workflows that previously took five days now completed in a day.
Startup Momentum and Funding
Rescale raised a $115 million Series D round in April 2025, bringing its total capital to over $260 million. Investors such as Nvidia, Applied Materials, Foxconn, and others participated.
Joris Poort, CEO and co-founder, emphasized that AI models trained via their platform can run in under a second and achieve ~98% or better accuracy compared to full-scale solvers.
In Forbes, NVIDIA CEO Jensen Huang stated:
“With Rescale’s full‑stack NVIDIA software and infrastructure, industries can push the boundaries of AI‑driven modeling and simulation—advancing discovery, design, and engineering at an unprecedented pace.” forbes.com
In Conclusion
Rescale’s platform truly aligns with its product claims. It delivers cloud-native HPC, AI physics, and workflow automation in a unified environment tailored for enterprise R&D. Through partnerships with NVIDIA and Navier AI, Rescale enables orders-of-magnitude speedups—with near-equivalent fidelity—to accelerate discovery and product design across a wide range of industries.
By building on simulation data, maintaining accuracy, and automating engineering workflows, Rescale is redefining the future of simulation-driven innovation.
This content originally appeared on DEV Community and was authored by Nick Johnson
Nick Johnson | Sciencx (2025-08-07T09:06:55+00:00) Reimagining Physics Simulations with AI: An In-Depth Look at Rescale’s Platform. Retrieved from https://www.scien.cx/2025/08/07/reimagining-physics-simulations-with-ai-an-in-depth-look-at-rescales-platform/
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