ArticleMachine learning for computational science and engineering2025
From PINNs to PIKANs: recent advances in physics-informed machine learning.
Article in Machine learning for computational science and engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational Architectures for Precision Dairy Nutrition Digital Twins: A Technical Review and Implementation Framework.Sensors (Basel, Switzerland) · 2025Pooled it
- A Practical Tutorial on Physics-Informed Networks for Pharmacometrics and Quantitative Systems Pharmacology.CPT: pharmacometrics & systems pharmacology · 2026Article
- Capturing the Multiscale Nature of Bone Behavior: Classical, Data-Driven and Hybrid Techniques.Annals of biomedical engineering · 2026Review
- Integration of Artificial Intelligence and Microfluidics for Drug Delivery Applications.Micromachines · 2026Review
- Physics-guided residual learning for phase-aware UAV trajectory prediction in urban environments.Scientific reports · 2026Article
- MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI.Science advances · 2026Article
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- A Multiscale Signaling-Biophysical Framework Reveals Mechanisms of Macrophage-Mediated RBC Clearance in Sickle Cell and Gaucher Disease.bioRxiv : the preprint server for biology · 2026Article
- Robust MR-AIV: A Systematic Study of Robustness Improvement and Sensitivity Analysis of MR-AIV.bioRxiv : the preprint server for biology · 2026Article
- Learning turbulent flows with generative models for super resolution and sparse flow reconstruction.Nature communications · 2026Article
- Research on precision prediction of heavy-duty lathes based on hybrid PINNS neural network.Scientific reports · 2026Article
- Representation meets optimization: Training PINNs and PIKANs for gray-box discovery in systems pharmacology.Computers in biology and medicine · 2026Article
- FMEnets: Flow, material, and energy networks for non-ideal plug flow reactor design.Chemical engineering science · 2026Article
- IGANets: Isogeometric analysis networks and their applications to linear structural analysis problems.Engineering with computers · 2026Article
- Toward Digital Twins for Optimal Radioembolization.PET clinics · 2026Review
- Knowledge embedded wind tunnel typical pressure vessel design optimization method research.PloS one · 2026Article
- A variational framework for residual-based adaptivity in neural PDE solvers and operator learning.NPJ artificial intelligence · 2026Article
- Physics-informed neural network framework for predicting drilling-induced delamination in GFRP composites.PloS one · 2026Article
- KKANs: Ku̇rková-Kolmogorov-Arnold networks and their learning dynamics.Neural networks : the official journal of the International Neural Network Society · 2025Article
- In Silico Post-screening of Anti-polymerization Agents to Treat Sickle Cell Disease.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offeringa promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Finally, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.