Public repository for the proposal “Physics-Informed Machine Learning Simulator for Wildfire Propagation” - MLJC University of Turin - ProjectX2020 Competition (UofT AI)

Latent Differential Equations models in Julia.

[𝐍𝐚𝐭𝐮𝐫𝐞 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐜𝐢𝐞𝐧𝐜𝐞] ⚡️ PSE/PSRN: Fast and efficient symbolic expression discovery through parallelized symbolic enumeration. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

Folax (Finite Operator Learning with JAX) is a framework for solving and optimizing PDEs by integrating machine learning with numerical methods in computational mechanics.

HookeAI: An open-source ADiMU framework

Graphorge: Open-source forge of Graph Neural Networks

A Physics-Informed Neural Network (PINN) implemented in PyTorch to solve the N-dimensional coupled spring-mass system ODEs.

Lagrangian and Hamiltonian Neural Ordinary Differential Equations (NODEs)

🔍 Solve N-dimensional coupled spring-mass systems using a Physics-Informed Neural Network in PyTorch, without needing ground-truth data.

Numerical solutions of several PDEs using Physics-Informed Neural Networks