A physics-informed neural network method for head-related transfer function upsampling
Going through the tutorial on Physics-informed Neural Networks: https://github.com/madagra/basic-pinn
Physics-Informed Neural Network modeling developing laminar pipe flow in a circular pipe, trained on boundary layer equations and boundary conditions without labeled data.
This repository contains all Assignments and Lecture Slides from the Physics Informed Machine learning course by Prof. Augustin Guibaud in Spring 2025 at NYU.
Physics informed neural networks (PINN) with a mathematically informed architecture for the nuclear decay equation
Physics-informed neural surrogate for structural analysis — 1000x faster than FEA with >99.9% accuracy
Physics-informed Bidirectional LSTM (DPRNet-Bi) for ventilator airway pressure prediction — deep learning + lung mechanics for biomedical time-series modeling. Validated on Kaggle Ventilator Pressure Prediction dataset with MIMIC-III cross-dataset evaluation.
Physics-Informed Neural Network framework for atmospheric CO2 flux inversion over Europe (ICOS network, 2019). Decoupled fossil/biospheric separation with HYSPLIT transport.
VICTOR (Variational Inference for Confined Tokamak Output Reconstruction) — A Physics-Informed Neural Network that reconstructs plasma emissivity on the WEST Tokamak in ~0.5ms, trained entirely without ground-truth labels. Enabling real-time fusion reactor diagnostics and disruption prevention.
JAX/Flax physics-informed neural network with jax2tf export — benchmark JAX vs PyTorch vs TensorFlow
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