Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Automatic Finite Difference PDE solving with Julia SciML

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

Code Repository for the paper "Mechanistic Neural Networks for Scientific Machine Learning", ICML 2024

Taylor mode automatic differentiation (jets) in PyTorch

Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting

DDPM-based U-Net transformer for fluid dynamics prediction, reproducing and extending DiffFluid. Validated on Navier-Stokes vorticity and Lattice Boltzmann (D2Q9) with corrected noise-prediction loss formulation.

Neural ODE-based State of Charge (SOC) estimation for Li-ion batteries using the NASA Battery Dataset. Built as a weekend project to explore learned dynamics for battery modeling, with visualizations designed for engineering audiences.

GPU-accelerated, fault-tolerant Schlieren/PIV shock tracking with interactive ROI, 1-px edges, and resumable training.

Structured ecosystem of 270+ AI systems spanning foundation models, agentic reasoning, reinforcement learning, generative architectures, scientific ML, and self-evolving neural systems. A research-driven lab exploring scalable intelligence.

PyTorch-based Mask R-CNN implementation for instance segmentation in scientific image datasets.

Physics-Informed Neural Networks with passivity constraints and ensemble uncertainty quantification for nonlinear inverse modeling.

🧬ML pipeline for predicting molecular melting points using RDKit features.

Uninet — short for "Unified Neural Network System" — is a GitHub project that brings together diverse machine learning tasks and neural network architectures under one umbrella.

Research-grade implementations of nonlinear dynamical systems, predictability, and regime behavior across complex systems (ecology, synthetic systems, and beyond)

Parametric PINN surrogate for flow over cylinders — 95% accuracy, 95%+ CFD speedup, <50ms inference

🔬 Predict molecular melting points with a robust machine learning pipeline that prioritizes reproducibility and efficient data handling.