Oct 21, 2022 · We develop a novel deep learning method for uncertainty quantification in stochastic partial differential equations based on Bayesian neural ...
Bayesian physics-informed neural networks (B-PINNs) have emerged as an efficient tool for uncertainty quantification in partial differential equations ...
Abstract. Bayesian physics-informed neural networks (B-PINNs) have emerged as an efficient tool for uncertainty quantification in partial differential equations ...
Feb 3, 2024 · This study develops nonlinear machine learning models to infer substrate depths by fusing sparse borehole logs with regional geospatial data. A ...
Oct 21, 2022 · ABSTRACT. We develop a novel deep learning method for uncertainty quantification in stochastic partial differ- ential equations based on ...
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We develop a novel deep learning method for uncertainty quantification in stochastic partial differential equations based on Bayesian neural network (BNN) ...
A robust uncertainty quantification (UQ) method should quantify both epistemic and aleatoric uncertainties associated with a system. The existing methods for UQ ...
Jan 30, 2024 · Bayesian physics-informed neural networks (B-PINNs) have emerged as an efficient tool for uncertainty quantification in partial differential ...
Oct 1, 2023 · A novel Bayesian Augmented-Learning framework, quantifying the uncertainty of spectral representations of stochastic processes in the presence ...
Uncertainty quantification (UQ) helps to make trustworthy predictions based on collected observations and uncertain domain knowledge. With increased usage.