Deep generative symbolic regression
Symbolic regression (SR) aims to discover concise closed-form mathematical equations
from data, a task fundamental to scientific discovery. However, the problem is highly …
from data, a task fundamental to scientific discovery. However, the problem is highly …
Discovering governing equations from partial measurements with deep delay autoencoders
J Bakarji, K Champion… - Proceedings of the …, 2023 - royalsocietypublishing.org
A central challenge in data-driven model discovery is the presence of hidden, or latent,
variables that are not directly measured but are dynamically important. Takens' theorem …
variables that are not directly measured but are dynamically important. Takens' theorem …
From biological data to oscillator models using SINDy
Periodic changes in the concentration or activity of different molecules regulate vital cellular
processes such as cell division and circadian rhythms. Developing mathematical models is …
processes such as cell division and circadian rhythms. Developing mathematical models is …
Deep learning and symbolic regression for discovering parametric equations
Symbolic regression is a machine learning technique that can learn the equations governing
data and thus has the potential to transform scientific discovery. However, symbolic …
data and thus has the potential to transform scientific discovery. However, symbolic …
Interpretable structural model error discovery from sparse assimilation increments using spectral bias‐reduced neural networks: A quasi‐geostrophic turbulence test …
R Mojgani, A Chattopadhyay… - Journal of Advances in …, 2024 - Wiley Online Library
Earth system models suffer from various structural and parametric errors in their
representation of nonlinear, multi‐scale processes, leading to uncertainties in their long …
representation of nonlinear, multi‐scale processes, leading to uncertainties in their long …
DISCOVER: Deep identification of symbolically concise open-form partial differential equations via enhanced reinforcement learning
The working mechanisms of complex natural systems tend to abide by concise partial
differential equations (PDEs). Methods that directly mine equations from data are called PDE …
differential equations (PDEs). Methods that directly mine equations from data are called PDE …
Discovering conservation laws using optimal transport and manifold learning
Conservation laws are key theoretical and practical tools for understanding, characterizing,
and modeling nonlinear dynamical systems. However, for many complex systems, the …
and modeling nonlinear dynamical systems. However, for many complex systems, the …
Data-driven discovery of linear dynamical systems from noisy data
In modern science and engineering disciplines, data-driven discovery methods play a
fundamental role in system modeling, as data serve as the external representations of the …
fundamental role in system modeling, as data serve as the external representations of the …
Governing equation discovery based on causal graph for nonlinear dynamic systems
D Jia, X Zhou, S Li, S Liu, H Shi - Machine Learning: Science and …, 2023 - iopscience.iop.org
The governing equations of nonlinear dynamic systems is of great significance for
understanding the internal physical characteristics. In order to learn the governing equations …
understanding the internal physical characteristics. In order to learn the governing equations …
Marrying Causal Representation Learning with Dynamical Systems for Science
D Yao, C Muller, F Locatello - arXiv preprint arXiv:2405.13888, 2024 - arxiv.org
Causal representation learning promises to extend causal models to hidden causal
variables from raw entangled measurements. However, most progress has focused on …
variables from raw entangled measurements. However, most progress has focused on …