Search Results for author:
Found 44 papers, 1 papers with code
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation
We propose a neural operator framework, termed mixture density nonlinear manifold decoder (MD-NOMAD), for stochastic simulators.
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations
The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared with traditional methods.
PhyPlan: Compositional and Adaptive Physical Task Reasoning with Physics-Informed Skill Networks for Robot Manipulators
PhyPlan leverages PINNs to simulate and predict outcomes of actions in a fast and accurate manner and uses MCTS for planning.
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.
Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator
We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which aims to bridge the gap between theoretical and practical implementation of Artificial Intelligence (AI) algorithms for mechanics applications.
Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression
This property of the proposed VSN makes it suitable for regression tasks, which is a weak point for the vanilla spiking neurons, all while keeping the energy budget low.
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.
Waveformer for modelling dynamical systems
In this work, we propose "waveformer", a novel operator learning approach for learning solutions of dynamical systems.
DPA-WNO: A gray box model for a class of stochastic mechanics problem
The well-known governing physics in science and engineering is often based on certain assumptions and approximations.
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.
Randomized prior wavelet neural operator for uncertainty quantification
In this paper, we propose a novel data-driven operator learning framework referred to as the \textit{Randomized Prior Wavelet Neural Operator} (RP-WNO).
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life
Therefore, the use of Explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL) in a digital twin system to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users.
Decision Making
Explainable Artificial Intelligence (XAI)
+1
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.
Physics-Informed Multi-Stage Deep Learning Framework Development for Digital Twin-Centred State-Based Reactor Power Prediction
In order to address this gap, this study specifically focuses on the "ML-driven prediction algorithms" as a viable component for the nuclear reactor operation while assessing the reliability and efficacy of the proposed model.
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.
Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code
To understand the potential of intelligent confirmatory tools, the U. S. Nuclear Regulatory Committee (NRC) initiated a future-focused research project to assess the regulatory viability of machine learning (ML) and artificial intelligence (AI)-driven Digital Twins (DTs) for nuclear power applications.
Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations
The primary idea here is to exploit DNN to model the missing physics.
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.
BYOLMed3D: Self-Supervised Representation Learning of Medical Videos using Gradient Accumulation Assisted 3D BYOL Framework
Supervised Learning algorithms require a large volumes of balanced data to learn robust representations.
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations
Neural network based data-driven operator learning schemes have shown tremendous potential in computational mechanics.
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.
Energy networks for state estimation with random sensors using sparse labels
Based on this technique we present two models for discrete and continuous prediction in space.
Koopman operator for time-dependent reliability analysis
Unlike purely data-driven approaches, the proposed approach is robust even in the presence of uncertainties; this renders the proposed approach suitable for time-dependent reliability analysis.
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems
Time dependent reliability analysis and uncertainty quantification of structural system subjected to stochastic forcing function is a challenging endeavour as it necessitates considerable computational time.
Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification
We propose a novel \textit{capsule} based deep encoder-decoder model for surrogate modeling and uncertainty quantification of systems in mechanics from sparse data.
Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification
The proposed GLU-net treats the uncertainty propagation problem as an image to image regression and hence, is extremely data efficient.
A deep learning based surrogate model for stochastic simulators
We propose a deep learning-based surrogate model for stochastic simulators.
Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems
For improving the predictive capability of the underlying physics, we first use machine learning algorithm to learn a mapping between the estimated state and the input (model-form error) and then introduce it into the governing equation as an additional term.
GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations
With recent developments in the field of artificial intelligence and machine learning, the solution of PDEs using neural network has emerged as a domain with huge potential.
Surrogate assisted active subspace and active subspace assisted surrogate -- A new paradigm for high dimensional structural reliability analysis
The basic premise is to train the surrogate model on a low-dimensional manifold, discovered using the active subspace algorithm.
Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system
In this paper, we propose a novel digital twin framework for stochastic nonlinear multi-degree of freedom (MDOF) dynamical systems.
State estimation with limited sensors -- A deep learning based approach
We illustrate that utilizing sequential data allows for state recovery from only one or two sensors.
Transfer learning based multi-fidelity physics informed deep neural network
This is followed by transfer learning where the low-fidelity model is updated by using the available high-fidelity data.
Machine learning based digital twin for dynamical systems with multiple time-scales
Our approach strategically separates into two components -- (a) a physics-based nominal model for data processing and response predictions, and (b) a data-driven machine learning model for the time-evolution of the system parameters.
Simulation free reliability analysis: A physics-informed deep learning based approach
Moreover, the primary bottleneck of solving reliability analysis problems, i. e., running expensive simulations to generate data, is eliminated with this method.
The role of surrogate models in the development of digital twins of dynamic systems
As digital twins are also expected to exploit data and computational methods, there is a compelling case for the use of surrogate models in this context.
Transfer learning enhanced physics informed neural network for phase-field modeling of fracture
While most of the PINN algorithms available in the literature minimize the residual of the governing partial differential equation, the proposed approach takes a different path by minimizing the variational energy of the system.
A Gaussian process latent force model for joint input-state estimation in linear structural systems
A novel methodology using Gaussian process latent force models is proposed to tackle the problem in a stochastic setting.
