Accelerating Physics Simulations with Machine Learning
We help engineering and research teams reduce simulation time by 10–100× using ML emulators, physics-informed neural networks, and high-performance code optimization. From CFD and FEM to climate models — we make slow simulations fast.
The Problem We Solve
Most engineering simulations take hours or days to run — slowing down innovation, R&D cycles, and decision-making.
Whether it's computational fluid dynamics (CFD), finite element analysis (FEM), electromagnetic simulations, or climate models, traditional physics-based solvers are computationally expensive. This creates bottlenecks for:
- Engineering teams running design optimization and parameter sweeps
- Research labs analyzing multi-physics systems
- Manufacturing companies needing real-time digital twins
- Climate and environmental teams running large-scale models
We solve this by building ML-driven surrogate models that replace slow simulations with fast, accurate predictions — while preserving the underlying physics.
What We Do
Three core solutions for accelerating physics-based simulations
ML Emulators for Physics-Based Systems
We build surrogate models and neural operators that replace computationally expensive simulations with fast ML predictions. Trained on physics data, these emulators deliver 10–100× speedups while maintaining accuracy for CFD, FEM, heat transfer, electromagnetics, and multi-physics systems. Ideal for design optimization, parameter sweeps, and real-time decision support.
Physics-Informed Neural Networks (PINNs)
We implement physics-informed ML models that incorporate governing equations directly into the learning process. Whether it's Navier-Stokes, Maxwell's equations, heat diffusion, or wave propagation, our PINNs solve partial differential equations efficiently while respecting physical constraints. Perfect for inverse problems, parameter identification, and scenarios with limited data.
Code Modernization & HPC Optimization
We modernize legacy scientific software (Fortran, C, Python) into optimized, GPU-accelerated frameworks. Our expertise in parallelization, memory optimization, and multi-architecture scaling transforms slow research codes into production-ready industrial tools. We handle numerical algorithm redesign, profiling, and deployment across HPC clusters and cloud infrastructure.
Who We Work With
Engineering Simulation Teams
Companies running CFD, FEM, or multi-physics simulations who need faster design iterations and optimization workflows.
Research Labs
Academic and industrial research groups working with computationally intensive physics models in climate, materials, energy, and aerospace.
Manufacturing & Energy Companies
Organizations deploying digital twins and real-time simulation systems for production optimization and predictive analytics.
Climate & Environmental Teams
Groups running large-scale climate models, ecosystem simulations, and environmental impact assessments requiring computational acceleration.
Why Choose Chakradhari
Deep Expertise in Numerical Physics + ML
We understand both the mathematics of physics-based simulations and the implementation of modern ML techniques — ensuring solutions are scientifically sound and computationally efficient.
HPC & GPU Optimization Experience
Proven track record in high-performance computing, multi-architecture code optimization, and large-scale computational workloads across diverse supercomputing environments.
Physics-Informed ML Integration
Unique ability to integrate scientific principles directly into machine learning models, creating surrogate systems that respect physical laws and domain constraints.
Proven Simulation Acceleration
Track record of accelerating real-world simulations across CFD, FEM, electromagnetics, and climate modeling — delivering measurable speedups and cost savings.
How We Work
From assessment to deployment — our systematic approach to simulation acceleration
Assessment & Profiling
We analyze your existing simulation workflows, identify computational bottlenecks, and quantify potential speedup opportunities through detailed profiling and benchmarking.
Solution Design & Validation
We develop custom ML emulators or optimize your simulation code, rigorously validating against ground truth physics to ensure accuracy and reliability across your parameter space.
Deployment & Integration
We integrate accelerated solutions into your existing infrastructure, provide comprehensive documentation, and support your team through deployment and scaling.
Our Team
Physicists and computational scientists with deep research expertise
Founded by researchers with backgrounds in computational physics, applied mathematics, and high-performance computing from leading institutions across India, Germany, Belgium, and the United States. Our team combines over 15 years of experience in scientific computing with practical expertise in delivering production-grade computational solutions.
We've worked extensively with large-scale simulations across diverse supercomputing architectures — from numerical weather prediction and climate modeling to quantum systems and fluid dynamics. This cross-domain experience enables us to tackle complex computational challenges across industries.
Numerical Physics
CFD, FEM, PDEs, multi-physics modeling
ML for Scientific Computing
PINNs, neural operators, surrogate modeling
HPC & Code Optimization
GPU acceleration, parallelization, performance tuning
Additional Capabilities
Complementary services supporting our core mission
Digital Twin Development
Building physics-driven digital twin platforms and decision-support systems that integrate real-time data with fast simulation engines for manufacturing, infrastructure, and environmental monitoring.
Computer Vision for Scientific Imaging
Developing ML pipelines for analyzing scientific images, satellite data, and experimental observations — from automated feature extraction to physics-informed image reconstruction.
Domain-Specific LLM Solutions
Fine-tuning and deploying large language models for technical documentation, code generation, and knowledge extraction in specialized scientific and engineering domains.
Education & Open Science
Democratizing access to advanced computational methods
Beyond our core commercial work, we're committed to making computational science more accessible through open-source tools, educational content, and training programs. We believe that cutting-edge methods shouldn't remain locked in academic papers or proprietary systems.
Current Initiatives In Progress
- Open-source toolboxes for satellite data processing and visualization
- Computational templates and numerical methods documentation
- Tutorial repositories for ML-driven simulation acceleration
Coming Soon
- YouTube series on numerical computing and physics-informed ML
- Interactive visualization tools for understanding complex simulations
- HPC optimization guides and case studies
Future Plans
- Specialized training programs for universities and industries
- Workshops on ML emulator development and deployment
- Custom boot camps for research groups
Ready to Accelerate Your Simulations?
Let's discuss how we can help you reduce simulation time and accelerate innovation.