Overview DeepXDE is a Python library for scientific machine learning. It solves forward and inverse ODEs, PDEs, integro-differential, fractional and stochastic PDEs with PINNs, and learns operators with DeepONet variants and multifidelity networks. It runs on TensorFlow 1.x, TensorFlow 2.x, PyTorch, JAX or PaddlePaddle backends.
Who it is for: Researchers and engineers solving PDEs with neural networks
Strengths Five selectable backends: TensorFlow 1.x/2.x, PyTorch, JAX, PaddlePaddle Geometry primitives plus CSG and point clouds, so no mesh generation is needed Covers PINN variants (fPINN, gPINN, hPINN) and DeepONet, MIONet, multifidelity networks Documented demos for forward, inverse and operator-learning problems; installs via pip or conda Weaknesses Requires installing a backend framework separately before DeepXDE works LGPL-2.1 license may constrain some proprietary or bundled use Library only: no GUI, server or pretrained model hub What it needs GPU optional Needs TensorFlow, PyTorch, JAX, Flax, Optax, PaddlePaddle Models: PINN, DeepONet, MIONet, MFNN, FNN Also in ML frameworks and libraries Rank Project Score 1 transformers Python library defining open models for text, vision, audio and multimodal tasks 94 out of 100 score 2 ultralytics Python library and CLI for training and running YOLO vision models 94 out of 100 score 3 pytorch Python tensor library with GPU support and tape-based autograd 93 out of 100 score # 4pytorch-image-models PyTorch library of image models, pretrained weights and training scripts 93 out of 100 score # 5supervision Python toolkit for loading, annotating and counting computer vision detections (opens in a new tab) 92 out of 100 score
How to choose Pick the framework your target models and papers already use. Check hardware support (CUDA, ROCm, Apple Silicon, TPU) for your machines. Look at release cadence; a framework that stalls strands your code. Health
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License in plain words LGPL-2.1 Custom terms
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