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Found 13 papers, 0 papers with code
Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data
This article proposes a generative adversarial wavelet neural operator (GAWNO) as a novel unsupervised deep learning approach for fault detection and isolation of multivariate time series processes. The GAWNO combines the strengths of wavelet neural operators and generative adversarial networks (GANs) to effectively capture both the temporal distributions and the spatial dependencies among different variables of an underlying system.
A foundational neural operator that continuously learns without forgetting
The proposed foundational model offers two key advantages: (i) it can simultaneously learn solution operators for multiple parametric PDEs, and (ii) it can swiftly generalize to new parametric PDEs with minimal fine-tuning.
A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data
Unlike existing neural network-based approaches, the proposed approach (a) yields an interpretable description of Lagrangian, (b) exploits Bayesian learning to quantify the epistemic uncertainty due to limited data, (c) automates the distillation of Hamiltonian from the learned Lagrangian using Legendre transformation, and (d) provides ordinary (ODE) and partial differential equation (PDE) based descriptions of the observed systems.
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.
A Bayesian Framework for learning governing Partial Differential Equation from Data
To accelerate the overall process, a variational Bayes-based approach for discovering partial differential equations is proposed.
Physics informed WNO
Deep neural operators are recognized as an effective tool for learning solution operators of complex partial differential equations (PDEs).
Discovering interpretable Lagrangian of dynamical systems from data
The Lagrangian are derived in interpretable forms, which also allows the automated discovery of conservation laws and governing equations of motion.
Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems
A framework for creating and updating digital twins for dynamical systems from a library of physics-based functions is proposed.
MAntRA: A framework for model agnostic reliability analysis
A two-stage approach is adopted: in the first stage, an efficient variational Bayesian equation discovery algorithm is developed to determine the governing physics of an underlying stochastic differential equation (SDE) from measured output data.
Model-agnostic stochastic model predictive control
The proposed approach first discovers \textit{interpretable} governing differential equations from data using a novel algorithm and blends it with a model predictive control algorithm.
Learning governing physics from output only measurements
The existing techniques for equations discovery are dependent on both input and state measurements; however, in practice, we only have access to the output measurements only.
Multi-fidelity wavelet neural operator with application to uncertainty quantification
However, this issue can be alleviated with the use of multi-fidelity learning, where a model is trained by making use of a large amount of inexpensive low-fidelity data along with a small amount of expensive high-fidelity data.
Wavelet neural operator: a neural operator for parametric partial differential equations
With massive advancements in sensor technologies and Internet-of-things, we now have access to terabytes of historical data; however, there is a lack of clarity in how to best exploit the data to predict future events.
