Results 111 to 120 of about 35,012 (233)
IEEE AccessIn this study, we discuss a mathematical framework to handle the inverse problem for the applications of partial differential equations (PDEs). In particular, we focus on wave equations and attempt to identify the wave parameters such as wave velocity ...
Alireza Pakravan
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Quantum Time‐Marching Algorithms for Solving Linear Transport Problems Including Boundary Conditions
International Journal for Numerical Methods in Engineering, Volume 127, Issue 8, 30 April 2026.ABSTRACT This article presents the first complete application of a quantum time‐marching algorithm for simulating multidimensional linear transport phenomena with arbitrary boundaries, whereby the success probabilities are problem intrinsic. The method adapts the linear combination of unitaries algorithm to block encode the diffusive dynamics, while ...
Sergio Bengoechea +2 more
wiley +1 more source
Robust Inverse Material Design With Physical Guarantees Using the Voigt‐Reuss Net
International Journal for Numerical Methods in Engineering, Volume 127, Issue 7, 15 April 2026.ABSTRACT We apply the Voigt‐Reuss net, a spectrally normalized neural surrogate introduced in [38], for forward and inverse mechanical homogenization with a key guarantee that all predicted effective stiffness tensors satisfy Voigt‐Reuss bounds in the Löwner sense during training, inference, and gradient‐driven optimization.
Sanath Keshav, Felix Fritzen
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DISCRETE NON-STANDARD FORMULATION OF PDE INVERSE PROBLEMS
, 2023 Cyr-Séraphin Ngamouyih Moussata +3 more
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HeliyonIn the modern era, artificial intelligence (AI) has been applied as one of the transformative factors for scientific research in many fields that could provide new solutions to extremely complicated and complex physical models.
Muflih Alhazmi +4 more
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BiLO: Bilevel Local Operator Learning for PDE Inverse Problems. Part I: PDE-Constrained Optimization
We propose a new neural network based method for solving inverse problems for partial differential equations (PDEs) by formulating the PDE inverse problem as a bilevel optimization problem. At the upper level, we minimize the data loss with respect to the PDE parameters.
Zhang, Ray Zirui +3 more
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Latent Neural Operator for Solving Forward and Inverse PDE Problems
Advances in Neural Information Processing Systems 37Neural operators effectively solve PDE problems from data without knowing the explicit equations, which learn the map from the input sequences of observed samples to the predicted values. Most existing works build the model in the original geometric space, leading to high computational costs when the number of sample points is large.
Wang, Tian, Wang, Chuang
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Inverse problems for infinite-dimensional transport PDEs on Wasserstein space
We develop a foundational framework for inverse problems governed by evolutionary partial differential equations (PDEs) on the Wasserstein space of probability measures. While the forward problems for such transport-type PDEs have been extensively and intensively studied, their corresponding inverse problems--which aim to reconstruct unknown operators,
Liu, Hongyu +2 more
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A mixed formulation of the Tikhonov regularization and its application to inverse PDE problems [PDF]
, 2018 Laurent Bourgeois, Arnaud Recoquillay
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