Computational Scientific Machine Learning Lab (CSML)

Computational Scientific Machine Learning Lab 🚀

Welcome to the official GitHub home of the CSML Lab at Rensselaer Polytechnic Institute (RPI). Our goal is to push the frontier of Scientific AI in Aerospace Engineering. We reside at the intersection of Physics-Informed AI, Data-Driven Dynamical Systems, Agentic AI, and Computational Fluid Dynamics.

🌐 Lab Website | 📧 Contact | 📍 RPI MANE | 🚫 Lab Member Access

🧬 Our Core Research Pillars

Machine Learning for Dynamical Systems / Koopman Operator / Data-Driven Control
- Phase I (2018) Long-Horizon + Jacobian Regularization
  - Long-time predictive modeling of nonlinear dynamical systems using neural networks
- Phase II (2019–2020) Stability + Time Delay Embedding
  - Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability
  - On the Structure of Time-delay Embedding in Linear Models of Non-linear Dynamical Systems
- Phase III (2021) Modes Selection
  - Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces
- Phase IV (2024) Multi-Attractor + Open Source
  - On the lifting and reconstruction of nonlinear systems with multiple invariant sets
  - PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
- Phase V (2025) Noise-Robust Koopman and Control
  - Learning Noise-Robust Stable Koopman Operator for Control With Hankel DMD
AI for PDE
- Phase I (2019) CNN Surrogates
  - Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks
- Phase II (2020) PINNs
- Phase III (2023) Bridging INR with Operator Learning
  - Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data
- Phase IV (2024) Apply in Plasma
  - Grad–Shafranov equilibria via data-free physics informed neural networks
- Phase V (2025–2026) Apply in Nuclear
  - Surrogate modeling of heat transfer using Fourier Basis-DeepONet with uncertainty quantification
  - Characterization of DeepONet Performance for Neutron Transport Modeling
CFD, Turbulence, Closures, Reduced-Order Models
- Phase I (2014–2015) High-Speed CFD / Rarefied Gas
- Phase II (2016) Compressible Turbulence
  - The role of bulk viscosity on the decay of compressible, homogeneous, isotropic turbulence
- Phase III (2017) ML turbulence modeling
  - Characterizing and improving predictive accuracy in shock-turbulent boundary layer interactions using data-driven models
  - Augmentation of turbulence models using field inversion and machine learning
- Phase IV (2018) Data-Driven Closures
  - Data-driven Discovery of Closure Models
- Phase V (2025) A Near-Optimal Low-Rank Representation
  - Beyond the Kolmogorov Barrier: A Learnable Weighted Hybrid Autoencoder for Model Order Reduction
Agentic AI Foam-Agent
- Phase I (2025) Agents for CFD
  - Foam-Agent: Towards Automated Intelligent CFD Workflows
  - FoamGPT: Fine-Tuning Large Language Model for Agentic Automation of CFD Simulations with OpenFOAM
- Phase II (2025) Ecosystems + Infrastructure in the era of LLMs
  - Code2MCP: Transforming Code Repositories into MCP Services
  - UniFoil: A Universal Dataset of Airfoils in Transitional and Turbulent Regimes

🛠 Tech Stack & Tools

🌟 Featured Repositories

Project	Description	Status
PyKoopman	The premier Python library for Koopman operator learning.	⭐ Active
Foam-Agent	Agentic AI framework for automated OpenFOAM simulations.	🚀 New
NIF	Neural Implicit Flow for mesh-agnostic reduced-order modeling.	📄 Paper
CFDLLMBench	Benchmarking LLMs on Computational Fluid Dynamics tasks.	📊 Research