Towards data-driven discovery of governing equations in geosciences
Governing equations are foundations for modelling, predicting, and understanding the Earth
system. The Earth system is undergoing rapid change, and the conventional approaches for …
system. The Earth system is undergoing rapid change, and the conventional approaches for …
Discovering stochastic partial differential equations from limited data using variational Bayes inference
We propose a novel framework for discovering Stochastic Partial Differential Equations
(SPDEs) from data. The proposed approach combines the concepts of stochastic calculus …
(SPDEs) from data. The proposed approach combines the concepts of stochastic calculus …
A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data
T Tripura, S Chakraborty - arXiv preprint arXiv:2310.06241, 2023 - arxiv.org
Learning and predicting the dynamics of physical systems requires a profound
understanding of the underlying physical laws. Recent works on learning physical laws …
understanding of the underlying physical laws. Recent works on learning physical laws …
BINDy--Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte-Carlo
MD Champneys, TJ Rogers - arXiv preprint arXiv:2408.08062, 2024 - arxiv.org
Model parsimony is an important\emph {cognitive bias} in data-driven modelling that aids
interpretability and helps to prevent over-fitting. Sparse identification of nonlinear dynamics …
interpretability and helps to prevent over-fitting. Sparse identification of nonlinear dynamics …
Data-driven discovery of interpretable Lagrangian of stochastically excited dynamical systems
Exploring the intersection of deterministic and stochastic dynamics, this paper delves into
Lagrangian discovery for conservative and non-conservative systems under stochastic …
Lagrangian discovery for conservative and non-conservative systems under stochastic …
Discovering stochastic partial differential equations from limited data using variational Bayes inference
We propose a novel framework for discovering Stochastic Partial Differential Equations
(SPDEs) from data. The proposed approach combines the concepts of stochastic calculus …
(SPDEs) from data. The proposed approach combines the concepts of stochastic calculus …
Data-driven recovery of PDE models and unveiling of solution interconnections
Z Lü, Y Zhang, X Zheng, L Duan - Nonlinear Dynamics, 2024 - Springer
In this paper, numerical differentiation, automatic differentiation, and multiple linear
regression techniques are integrated to address two key issues: the identification of latent …
regression techniques are integrated to address two key issues: the identification of latent …
A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data
T Tripura, S Chakraborty - Mechanical Systems and Signal Processing, 2024 - Elsevier
Learning and predicting the dynamics of physical systems requires a profound
understanding of the underlying physical laws. Recent works on learning physical laws …
understanding of the underlying physical laws. Recent works on learning physical laws …
Discovering governing equation in structural dynamics from acceleration-only measurements
C Alvares, S Chakraborty - arXiv preprint arXiv:2407.13704, 2024 - arxiv.org
Over the past few years, equation discovery has gained popularity in different fields of
science and engineering. However, existing equation discovery algorithms rely on the …
science and engineering. However, existing equation discovery algorithms rely on the …
Numerical Differentiation by Integrated Series Expansion (NDBISE) in the Context of Ordinary Differential Equation Estimation Problems
O Strebel - 2024 - researchsquare.com
Parameter or model estimation of ordinary differential equations (ODE) nowadays frequently
involves the numerical calculation of derivatives from noisy data. This study presents a novel …
involves the numerical calculation of derivatives from noisy data. This study presents a novel …