PinnedPublished inTDS ArchivePhysics-Informed Neural Networks: An Application-Centric GuideA comprehensive overview of PINN’s real-world success storiesFeb 9, 2024A response icon7Feb 9, 2024A response icon7
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, 2023A response icon3May 15, 2023A response icon3
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, 2023A response icon4Jul 7, 2023A response icon4
PinnedPublished inTDS ArchiveUncertainty Quantification ExplainedA practice for making reliable model-based predictionsJul 20, 2020A response icon2Jul 20, 2020A response icon2
How LLMs Are Transforming Anomaly Detection (With Real Examples)7 Emerging Patterns Every Practitioner Should Know2d ago2d ago
Small Language Models: Why Less Is Actually MoreHow targeted design and smart engineering are making AI more practical, private, and accessibleMay 27A response icon8May 27A response icon8
Using Physics-Informed Neural Networks as Surrogate Models: From Promise to PracticalityWhen, Why, and How to Use Them in Your Engineering WorkflowMay 20A response icon5May 20A response icon5
Physics-Informed Neural Networks for Anomaly Detection: A Practitioner’s GuideThe why, what, how, and when to apply physics-guided anomaly detectionApr 24A response icon12Apr 24A response icon12
Published inData Science CollectiveSecurity Vulnerabilities in LLM-Powered Multi-Agent Systems: What Developers Need to KnowUnderstanding the threats landscape via three practical applicationsApr 4A response icon1Apr 4A response icon1
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