A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems
DOI10.4208/cicp.OA-2021-0088WikidataQ110541559 ScholiaQ110541559MaRDI QIDQ5065171
K. B. Nakshatrala, M. K. Mudunuru, Unnamed Author
Publication date: 18 March 2022
Published in: Communications in Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2101.04227
pattern recognitionnetworksdeep learningnon-negative solutionslong short-term memory (LSTM)convolutional neural networks (CNN)reactive-transportspatial-temporal forecasting
Computational learning theory (68Q32) Artificial neural networks and deep learning (68T07) PDEs in connection with fluid mechanics (35Q35) Reaction-diffusion equations (35K57) Classical flows, reactions, etc. in chemistry (92E20) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Finite difference methods for initial value and initial-boundary value problems involving PDEs (65M06) Finite element methods applied to problems in fluid mechanics (76M10) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Chemically reacting flows (80A32) Reaction effects in flows (76V05) Software, source code, etc. for problems pertaining to partial differential equations (35-04) PDEs in connection with classical thermodynamics and heat transfer (35Q79) Initial-boundary value problems for second-order parabolic systems (35K51)
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