Results 11 to 20 of about 1,345 (197)

QEKI: A Quantum–Classical Framework for Efficient Bayesian Inversion of PDEs [PDF]

Entropy
Solving Bayesian inverse problems efficiently stands as a major bottleneck in scientific computing. Although Bayesian Physics-Informed Neural Networks (B-PINNs) have introduced a robust way to quantify uncertainty, the high-dimensional parameter spaces ...
Jiawei Yong, Sihai Tang
doaj   +2 more sources

The Hadamard-PINN for PDE inverse problems: Convergence with distant initial guesses

Examples and Counterexamples
This paper presents the Hadamard-Physics-Informed Neural Network (H-PINN) for solving inverse problems in partial differential equations (PDEs), specifically the heat equation and the Korteweg–de Vries (KdV) equation.
Yohan Chandrasukmana, Helena Margaretha, Kie Van Ivanky Saputra   +2 more
doaj   +2 more sources

Neural Inverse Operators for solving PDE Inverse Problems

, 2023
Data and models for the paper "Neural Inverse Operators for solving PDE Inverse Problems"
Molinaro, Roberto, Yang, Yunan, Engquist, Björn, Mishra, Siddhartha   +3 more
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Inverse Source Problems for Degenerate Time-Fractional PDE [PDF]

Progress in Fractional Differentiation and Applications, 2022
12 pages, 8 ...
Al-Salti, Nasser, Karimov, Erkinjon
openaire   +2 more sources

Structured Random Sketching for PDE Inverse Problems [PDF]

SIAM Journal on Matrix Analysis and Applications, 2020
For an overdetermined system $\mathsf{A}\mathsf{x} \approx \mathsf{b}$ with $\mathsf{A}$ and $\mathsf{b}$ given, the least-square (LS) formulation $\min_x \, \|\mathsf{A}\mathsf{x}-\mathsf{b}\|_2$ is often used to find an acceptable solution $\mathsf{x}$.
Ke Chen, Qin Li, Kit Newton, Stephen J. Wright   +3 more
openaire   +3 more sources

Enhancing Computational Accuracy in Surrogate Modeling for Elastic–Plastic Problems by Coupling S-FEM and Physics-Informed Deep Learning

Mathematics, 2023
Physics-informed neural networks (PINNs) provide a new approach to solving partial differential equations (PDEs), while the properties of coupled physical laws present potential in surrogate modeling.
Meijun Zhou, Gang Mei, Nengxiong Xu
doaj   +1 more source

Inverse coefficient problem for differential equation in partial derivatives of a fourth order in time with integral over-determination

Қарағанды университетінің хабаршысы. Математика сериясы, 2022
Derivatives in time of higher order (more than two) arise in various fields such as acoustics, medical ultrasound, viscoelasticity and thermoelasticity.
M.J. Huntul, I. Tekin
doaj   +1 more source

Approximation of Bayesian Inverse Problems for PDEs [PDF]

SIAM Journal on Numerical Analysis, 2010
35 pages, 3 ...
Cotter, S. L., Dashti, M., Stuart, A. M.
openaire   +4 more sources

A deep neural network approach for parameterized PDEs and Bayesian inverse problems

Machine Learning: Science and Technology, 2023
We consider the simulation of Bayesian statistical inverse problems governed by large-scale linear and nonlinear partial differential equations (PDEs). Markov chain Monte Carlo (MCMC) algorithms are standard techniques to solve such problems.
Harbir Antil, Howard C Elman, Akwum Onwunta, Deepanshu Verma   +3 more
doaj   +1 more source

An efficient algorithm for some highly nonlinear fractional PDEs in mathematical physics. [PDF]

PLoS ONE, 2014
In this paper, a fractional complex transform (FCT) is used to convert the given fractional partial differential equations (FPDEs) into corresponding partial differential equations (PDEs) and subsequently Reduced Differential Transform Method (RDTM) is ...
Jamshad Ahmad, Syed Tauseef Mohyud-Din
doaj   +1 more source