A systematic survey of physics-knowledge + ML methods for PDE prediction, splitting the field into architectural-level (loss, structured prediction, augmentation) and data-level (multi-task, meta, contextual) approaches, with industrial perspective and open-source ecosystem review.
arXiv preprint (copyright held by authors)Physics-informed MLPDEMulti-task Learning
A survey developing a unified, concept-driven view of major continuous-time machine learning branches, characterizing their mathematical relationships and design trade-offs.
See LICENSE filecontinuous-time modelsneural ODEstime series
A systematic review of physics-informed machine learning applications in electricity system design, monitoring, and control, focusing on hybrid architectures and physics constraint embedding methods.
See LICENSE filePhysics-Informed LearningElectricity SystemsElectromagnetic Simulation
A systematic survey of operator learning covering the theory, architectures and error analysis of DeepONet, FNO and their variants - the reference entry point to learning infinite-dimensional maps.
arXiv preprint (copyright held by authors)Operator LearningSurveyDeepONet
A free online textbook from TU Munich covering physics + deep learning: differentiable physics, PINNs, neural operators and turbulence modeling, with runnable code.
A classic Chinese structured tutorial implementing PINN from minimal dependencies, paired with Raissi's original repo - the best start for Chinese-speaking learners.
PhD course lecture notes from University of Bozen/Bolzano covering Physics Informed Neural Networks and Neural Operators with PyTorch implementation tutorials.
