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 CollectiveSecurity Vulnerabilities in LLM-Powered Multi-Agent Systems: What Developers Need to KnowUnderstanding the threats landscape via three practical applicationsApr 41Apr 41
Published inAI AdvancesSurrogate Modeling: The Secret to Faster, Smarter EngineeringIts fundamentals, capabilities, and engineering applicationsMar 65Mar 65
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 shineFeb 243Feb 243
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, 20243Jan 12, 20243
