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Found 7 papers, 1 papers with code
Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems
Uncertainty in the inverse solution is quantified in terms of the posterior distribution of CKLE coefficients, and we sample the posterior by solving a randomized PICKLE minimization problem, formulated by adding zero-mean Gaussian perturbations in the PICKLE loss function.
Physics-informed machine learning
Uncertainty Quantification
Online Real-time Learning of Dynamical Systems from Noisy Streaming Data: A Koopman Operator Approach
Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc.
Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems
Time series forecasting remains a central challenge problem in almost all scientific disciplines.
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines.
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
We propose a new forecasting method for predicting load demand and generation scheduling.
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence
In this work, we propose a new Gaussian process regression (GPR)-based multifidelity method: physics-informed CoKriging (CoPhIK).
Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks
We employ physics informed DNNs to estimate the unknown space-dependent diffusion coefficient in a linear diffusion equation and an unknown constitutive relationship in a non-linear diffusion equation.
Analysis of PDEs Computational Physics
