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PinnedInTDS ArchivebyShuai Guo, PhD·May 15, 2023Unraveling the Design Pattern of Physics-Informed Neural Networks: Series 01Optimizing the residual point distribution to boost PINN training efficiency and accuracyA response icon3A response icon3
PinnedInTDS ArchivebyShuai Guo, PhD·Jul 7, 2023Operator Learning via Physics-Informed DeepONet: Let’s Implement It From ScratchA deep dive into the DeepONets, physics-informed neural networks, and physics-informed DeepONetsA response icon4A response icon4
PinnedInTDS ArchivebyShuai Guo, PhD·Jul 20, 2020Uncertainty Quantification ExplainedA practice for making reliable model-based predictionsA response icon2A response icon2
InAI AdvancesbyShuai Guo, PhD·5d agoHow to Use a Time Series Foundation Model for Anomaly DetectionA practitioner’s guide built on Chronos-2 from AWS.A response icon3A response icon3
Shuai Guo, PhD·May 27, 2025Small Language Models: Why Less Is Actually MoreHow targeted design and smart engineering are making AI more practical, private, and accessibleA response icon9A response icon9
Shuai Guo, PhD·May 20, 2025Using Physics-Informed Neural Networks as Surrogate Models: From Promise to PracticalityWhen, Why, and How to Use Them in Your Engineering WorkflowA response icon5A response icon5
Shuai Guo, PhD·Apr 24, 2025Physics-Informed Neural Networks for Anomaly Detection: A Practitioner’s GuideThe why, what, how, and when to apply physics-guided anomaly detectionA response icon10A response icon10
InData Science CollectivebyShuai Guo, PhD·Apr 4, 2025Security Vulnerabilities in LLM-Powered Multi-Agent Systems: What Developers Need to KnowUnderstanding the threats landscape via three practical applicationsA response icon1A response icon1
InAI AdvancesbyShuai Guo, PhD·Mar 6, 2025Surrogate Modeling: The Secret to Faster, Smarter EngineeringIts fundamentals, capabilities, and engineering applicationsA response icon6A response icon6