Extracting Interpretable Physical Parameters from Spatiotemporal Systems Using Unsupervised Learning
出版年份 2020 全文链接
标题
Extracting Interpretable Physical Parameters from Spatiotemporal Systems Using Unsupervised Learning
作者
关键词
-
出版物
Physical Review X
Volume 10, Issue 3, Pages -
出版商
American Physical Society (APS)
发表日期
2020-09-12
DOI
10.1103/physrevx.10.031056
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Discovering Physical Concepts with Neural Networks
- (2020) Raban Iten et al. PHYSICAL REVIEW LETTERS
- Predictive large-eddy-simulation wall modeling via physics-informed neural networks
- (2019) X. I. A. Yang et al. Physical Review Fluids
- Data-driven discovery of PDEs in complex datasets
- (2019) Jens Berg et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Emergent Schrödinger equation in an introspective machine learning architecture
- (2019) Ce Wang et al. Science Bulletin
- Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
- (2019) Samuel H. Rudy et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Uniformly accurate machine learning-based hydrodynamic models for kinetic equations
- (2019) Jiequn Han et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network
- (2019) Zichao Long et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks
- (2018) Tianfan Xue et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
- (2018) Christoph Wehmeyer et al. JOURNAL OF CHEMICAL PHYSICS
- Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
- (2018) Jaideep Pathak et al. PHYSICAL REVIEW LETTERS
- Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
- (2018) Jin-Long Wu et al. Physical Review Fluids
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- (2018) M. Raissi et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
- (2017) Anuj Karpatne et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Data-driven discovery of partial differential equations
- (2017) Samuel H. Rudy et al. Science Advances
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- (2016) Julia Ling et al. JOURNAL OF FLUID MECHANICS
- Meep: A flexible free-software package for electromagnetic simulations by the FDTD method
- (2009) Ardavan F. Oskooi et al. COMPUTER PHYSICS COMMUNICATIONS
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started