Capabilities
Seven areas of deep expertise. Click any one to learn more.
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Scalable Quantitative Modeling & Simulations
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Modeling the real world: reliably and at scale.
We design and implement quantitative models and software to simulate complex systems — physical processes, engineered systems, or data-driven phenomena — grounded in first principles and enhanced by data.
We specialize in PDE-based modeling and simulation, with scalable implementations using tools like FEniCS (finite elements), OpenFOAM (finite volumes), and custom solvers, spanning deterministic and stochastic systems, hybrid physics-ML architectures, and multiscale formulations.
From early prototyping to production-scale simulation, we support the full modeling lifecycle across domains and deployment environments.
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Machine Learning & AI for Scientific Data
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Turning scientific data into predictive power.
From physical measurements to simulation outputs, scientific data is everywhere — but traditional models often oversimplify complex dynamics or demand excessive compute. We build custom machine learning tools tailored to your scientific workflows.
Our approach draws on scientific machine learning (SciML), where first-principles physics meets data-driven modeling. Whether you're accelerating a numerical solver, building a digital twin, or learning unknown dynamics from experiments, we develop models that are accurate, efficient, and physically grounded.
Use cases include foundation models, design optimization, and uncertainty quantification — all powered by modern ML for science.
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Agentic AI for Computational Workflows
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Autonomous agents that run your computational workflows.
We build AI agents that turn a plain-language goal into executed numerical work — planning the steps, calling the right tools (solvers, HPC jobs, ML models, data pipelines), interpreting the results, and iterating until the objective is met.
Our agents are grounded in scientific rigor and your domain constraints: they reason over real physics and data, keep a human in the loop where it matters, and produce results you can trace and trust. The outcome is faster exploration, less manual orchestration, and expert workflows your whole team can run.
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Custom Algorithms & Numerical Solvers
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Solvers that fit your problem, not the other way around.
We have decades of experience designing state-of-the-art numerical and machine learning algorithms for scientific and engineering applications — methods aligned with the structure and demands of your problem, not generic off-the-shelf tools.
Our expertise spans fast linear and nonlinear solvers, domain-specific PDE discretizations, adaptive time-stepping, and particle-based simulation. We also build scalable surrogate models, customized ML optimizers, advanced sampling techniques, and uncertainty quantification workflows for deterministic and stochastic systems.
From multiscale dynamics to PDE-constrained optimization, we develop robust computational tools grounded in scientific rigor and algorithmic innovation.
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Uncertainty Quantification & Probabilistic Inference
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Predictions without uncertainty are incomplete.
We go beyond point estimates to deliver confidence — quantifying how much trust you can place in your numerical models and ML predictions. Using Bayesian inference, stochastic sampling, and uncertainty quantification, we capture uncertainty from parameter calibration to predictive distributions.
Our methods address out-of-sample generalization and rare-event analysis. Whether you're solving PDEs with uncertain inputs or deploying ML in high-risk settings, our probabilistic approach delivers predictions that are robust, transparent, and decision-ready.
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GPU (CUDA) & HPC Integration
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Speed is strategy.
In modern computational science and machine learning, performance is foundational. We help you unlock the full power of your hardware by accelerating models and simulations with GPU computing, CUDA programming, and high-performance computing.
From custom CUDA kernels and GPU-enabled ML to parallel solvers and large-scale pipelines, we optimize your code for modern architectures. Whether you're handling terabytes of data, simulating PDEs at scale, or running ML workflows on Slurm, we build for performance, efficiency, and scalability.
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Onsite or Hosted Deployments
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Seamless deployment, your way.
We don't just build models — we help you integrate them into your workflow. Whether you need an onsite installation, a private cloud setup, or a lightweight containerized service, we deliver where and how you need it.
Our deployment support spans local execution to scalable hosted platforms. We work closely with your team for smooth integration with existing systems — running simulations, launching ML services, or automating pipelines — so our tools deliver value from day one.
Engagements include complete standalone solutions, onsite integration with maintenance, and features on demand — all tailored to your needs and form of execution.
