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Pennsylvania State University
- https://romit-maulik.github.io
Stars
A dataset of physical properties of the ocean, waves, and sea ice, with hydrological and atmospheric forcing, optimized for machine learning.
Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations
The quantum circuit and post-processing code for the geometric quantum encoding of a turbulent field.
High‑performance multiphysics CFD framework for turbulent reacting multiphase flows from low to high Mach number
Large-scale Time-series Dataset Towards Next-Generation Global Station Weather Forecasting Benchmark
This project contains a simulation framework for leveraging deep reinforcement learning in studying of the fluid dynamics. Stable BaseLine 3 and WaterLily are essential.
Weak SINDy model discovery for atmospheric data.
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
United collection of hybrid Central solvers - one-phase, two-phase and multicomponent versions
Official implementation of Diffusion Graph Networks (DGNs)
Code for the paper "Generative AI for fast and accurate statistical computation of fluids"
A tutorial showcasing the process of scaling up a SciML algorithm for multi-node training
Data-driven Reynolds-averaged turbulence modeling with generalizable non-linear correction and uncertainty quantification using Bayesian deep learning
Accelerated fulid-structure interaction (FSI) simulation using immersed boundary lattice Boltzmann method (IB-LBM) powered by JAX.
Control of Hypersonic Shock-Wave/Laminar Boundary-Layer Interaction using Deep Reinforcement Learning
Generalizing CNNs to Graphs with Learnable Neighborhood Quantization
[CVPR 2025 Oral] Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing
Implementation of the Prithvi WxC Foundation Model and Downstream Tasks
Software for training, running and analyzing a Deep Learning Earth System Model (DLESyM)
PyTorch implementation of DANN (Domain-Adversarial Training of Neural Networks)
A fully-differentiable compressible reacting flow solver based on JAX-AMR