Results 1 to 10 of about 261 (91)
Mathematical and Computational Applications, 2023 Physics-Informed Neural Networks (PINNs) are a new class of machine learning algorithms that are capable of accurately solving complex partial differential equations (PDEs) without training data.
Jacques Francois Du Toit, Ryno Laubscher
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A General Method for the Solution of Inverse Problems in Transport Phenomena
Chemical Engineering Transactions, 2015 The typical inverse problems in transport phenomena are given by partial differential equations with unknown boundary conditions, which are to be estimated from measurements corresponding to solutions of the PDEs or of their gradients.
M. Vocciante, A. Reverberi, V. Dovi
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AIChE Journal, EarlyView.ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho +4 more
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Inherited metabolic epilepsies–established diseases, new approaches
Epilepsia Open, EarlyView.Abstract Inherited metabolic epilepsies (IMEs) represent the inherited metabolic disorders (IMDs) in which epilepsy is a prevailing component, often determining other neurodevelopmental outcomes associated with the disorder. The different metabolic pathways affected by individual IMEs are the basis of their rarity and heterogeneity.
Itay Tokatly Latzer, Phillip L. Pearl
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Energy Science &Engineering, EarlyView.Mine‐water immersion tests reveal pronounced coal weakening (vs. minor concrete degradation), identifying coal pillars as the stability‐limiting component in composite dams. A coupled FEINN framework quantifies extreme‐pressure stability and ranks multi‐parameter designs via a normalized multi‐indicator scheme, enabling optimized dam configuration for ...
He Wen +6 more
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Subspace Acceleration for Efficient Nonlinear Water Wave Simulation
International Journal for Numerical Methods in Fluids, EarlyView.We introduce an exponentially weighted subspace acceleration technique to reduce GMRES iterations for solving the Poisson equation with time‐dependent coefficients in nonlinear, dispersive free‐surface flows governed by the incompressible Navier‐Stokes equations. The method significantly reduces memory requirements and computational complexity compared
Rasmus Kleist Hørlyck Sørensen +3 more
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Closing the Loop in Precision Oncology: A Digital Twin‐Driven Paradigm for Dynamic Decision‐Making
iMetaMed, EarlyView.This review introduces the Closed‐Loop Intelligent Oncology System (CIOS), a five‐layer framework integrating digital twins and AI to enable adaptive, data‐driven cancer treatment. By synthesizing advances in multimodal perception, mechanistic simulation, and safe reinforcement learning, CIOS charts a roadmap toward dynamic, personalized oncology ...
Junye Zhu +3 more
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Strategic Change, EarlyView.ABSTRACT Digital platform (DP) enterprises have risen to the top of the global economy by inverting traditional business models. They earn money through matchmaking, transaction facilitation, and efficient orchestration of other stakeholders' resources.
Lukas R. G. Fitz, Jochen Scheeg
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Computing Skinning Weights via Convex Duality
Computer Graphics Forum, EarlyView.We present an alternate optimization method to compute bounded biharmonic skinning weights. Our method relies on a dual formulation, which can be optimized with a nonnegative linear least squares setup. Abstract We study the problem of optimising for skinning weights through the lens of convex duality.
J. Solomon, O. Stein
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Reinforcement Learning for Jump‐Diffusions, With Financial Applications
Mathematical Finance, EarlyView.ABSTRACT We study continuous‐time reinforcement learning (RL) for stochastic control in which system dynamics are governed by jump‐diffusion processes. We formulate an entropy‐regularized exploratory control problem with stochastic policies to capture the exploration–exploitation balance essential for RL.
Xuefeng Gao, Lingfei Li, Xun Yu Zhou
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