Discovering causal relations and equations from data
Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …
questions about why natural phenomena occur and to make testable models that explain the …
Physics-informed machine learning for structural health monitoring
The use of machine learning in structural health monitoring is becoming more common, as
many of the inherent tasks (such as regression and classification) in developing condition …
many of the inherent tasks (such as regression and classification) in developing condition …
A sparse Bayesian framework for discovering interpretable nonlinear stochastic dynamical systems with Gaussian white noise
T Tripura, S Chakraborty - Mechanical Systems and Signal Processing, 2023 - Elsevier
Extracting governing physics from data is a key challenge in many areas of science and
technology. The existing techniques for equation discovery are mostly applicable to …
technology. The existing techniques for equation discovery are mostly applicable to …
On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression
This paper presents the use of spike-and-slab (SS) priors for discovering governing
differential equations of motion of nonlinear structural dynamic systems. The problem of …
differential equations of motion of nonlinear structural dynamic systems. The problem of …
Sparse Bayesian learning of explicit algebraic Reynolds-stress models for turbulent separated flows
Abstract A novel Sparse Bayesian Learning (SBL) framework is introduced for generating
stochastic Explicit Algebraic Reynolds Stress (EARSM) closures for the Reynolds-Averaged …
stochastic Explicit Algebraic Reynolds Stress (EARSM) closures for the Reynolds-Averaged …
Sparse identification for ball-screw drives considering position-dependent dynamics and nonlinear friction
X Liu, Y Li, Y Cheng, Y Cai - Robotics and Computer-Integrated …, 2023 - Elsevier
Establishing accurate dynamic models in a form that is suitable for integration with model-
based control methods, is of great significance for further improving the dynamic motion …
based control methods, is of great significance for further improving the dynamic motion …
Koopman operator learning using invertible neural networks
In Koopman operator theory, a finite-dimensional nonlinear system is transformed into an
infinite but linear system using a set of observable functions. However, manually selecting …
infinite but linear system using a set of observable functions. However, manually selecting …
MAntRA: A framework for model agnostic reliability analysis
We propose a novel model-agnostic data-driven reliability analysis framework for time-
dependent reliability analysis. The proposed approach–referred to as MAntRA–combines …
dependent reliability analysis. The proposed approach–referred to as MAntRA–combines …
[HTML][HTML] Sparse Bayesian machine learning for the interpretable identification of nonlinear structural dynamics: Towards the experimental data-driven discovery of a …
Data-driven discovery of governing laws for complex nonlinear structural dynamic systems
remains a challenging issue of paramount importance. This work addresses the above issue …
remains a challenging issue of paramount importance. This work addresses the above issue …
Discussing the spectra of physics-enhanced machine learning via a survey on structural mechanics applications
The intersection of physics and machine learning has given rise to the physics-enhanced
machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the …
machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the …