ArticleProceedings of the National Academy of Sciences of the United States of America2024
Learning dynamical systems from data: An introduction to physics-guided deep learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Improvement in model flexibility reveals signaling pathway in T cell responses to pulsatile stimuli.NPJ systems biology and applications · 2026Article
- A novel approach to quantify out-of-distribution uncertainty in Neural and Universal Differential Equations.NPJ systems biology and applications · 2026Article
- Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Physics-guided estimation of freight vehicle loading status using digital tachograph data.Scientific reports · 2026Article
- Article
- Dynamic response and parameter identification of an impact oscillator with a one-sided elastic constraint.Nonlinear dynamics · 2026Article
- Dynamic graph vector autoregression for characterizing cardiac electromechanical dynamics during incremental exercise from the joint phase space of electrocardiogram and phonocardiogram.Frontiers in physiology · 2026Article
- Enhanced Chroma-YOLO Framework for Effective Defect Detection and Fatigue Life Prediction in 3D-Printed Polylactic Acid.Materials (Basel, Switzerland) · 2025Article
- Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations.PLoS computational biology · 2025Article
- Biological physics to uncover cell signaling.Biophysical reviews · 2025Review
- A review of plant leaf disease identification by deep learning algorithms.Frontiers in plant science · 2025Review
- Response estimation and system identification of dynamical systems via physics-informed neural networks.Advanced modeling and simulation in engineering sciences · 2025Article
- Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?International journal of molecular sciences · 2024Review
- Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning.Journal of biomechanical engineering · 2024Article
- Machine learning meets physics: A two-way street.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach.Sensors (Basel, Switzerland) · 2023Article
- Machine learning to optimize additive manufacturing for visible photonics.Nanophotonics (Berlin, Germany) · 2023Article
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Authors and funding
2 authors.
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Abstract
Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive numerical integration, requiring significant computational resources and domain expertise. While deep learning (DL) provides efficient alternatives for modeling complex dynamics, they require a large amount of labeled training data. Furthermore, its predictions may disobey the governing physical laws and are difficult to interpret. Physics-guided DL aims to integrate first-principled physical knowledge into data-driven methods. It has the best of both worlds and is well equipped to better solve scientific problems. Recently, this field has gained great progress and has drawn considerable interest across discipline Here, we introduce the framework of physics-guided DL with a special emphasis on learning dynamical systems. We describe the learning pipeline and categorize state-of-the-art methods under this framework. We also offer our perspectives on the open challenges and emerging opportunities.
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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.