PinnedPublished inTDS ArchivePhysics-Informed Neural Networks: An Application-Centric GuideA comprehensive overview of PINN’s real-world success storiesFeb 9, 20247Feb 9, 20247
PinnedPublished inTDS ArchiveUnraveling the Design Pattern of Physics-Informed Neural Networks: Series 01Optimizing the residual point distribution to boost PINN training efficiency and accuracyMay 15, 20233May 15, 20233
PinnedPublished inTDS ArchiveOperator Learning via Physics-Informed DeepONet: Let’s Implement It From ScratchA deep dive into the DeepONets, physics-informed neural networks, and physics-informed DeepONetsJul 7, 20234Jul 7, 20234
PinnedPublished inTDS ArchiveUncertainty Quantification ExplainedA practice for making reliable model-based predictionsJul 20, 20202Jul 20, 20202
Published inData Science CollectiveThe Reality of Physics-Informed Neural Networks: Challenges, Alternatives, and Promising Use CasesWhy PINNs aren’t always the answer and when they truly shine3d ago13d ago1
Published inData Science CollectiveMulti-Agent System Powered by Large Language Models: An Innovation GuideWhat is it, how to innovate with it, and what hidden pitfalls to avoidFeb 131Feb 131
Published inData Science CollectiveSupercharging Physics-Informed Neural Networks Development with DeepSeek-R1Leveraging reasoning LLMs as a problem-solving partner for PINN development.Feb 61Feb 61
Published inTDS ArchiveModeling Dynamical Systems With Neural ODE: A Hands-on GuideConcepts, case studies, step-by-step implementationsJan 12, 20242Jan 12, 20242
Published inTDS ArchiveBuilding An Expert GPT in Physics-Informed Neural Networks, with GPTsA customized copilot for streamlining PINN research and developmentNov 18, 20234Nov 18, 20234
Published inTDS ArchiveWhen AutoML Meets Large Language ModelLeveraging the power of LLMs to guide hyperparameter searchesOct 5, 20234Oct 5, 20234
