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Deep Operator Network (DeepONet), a recently introduced deep learning operator network, approximates linear and nonlinear solution operators by taking parametric functions (infinite-dimensional objects) as inputs and mapping them to solution functions in contrast to classical neural networks that need re-training for every new set of parametric inputs.
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
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Lstm-based encoder-decoder for multi-sensor anomaly detection
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Deep learning-based crack damage detection using convolutional neural networks
Young-Jin Cha, Wooram Choi, and Oral Büyüköztürk · 2017
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Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types
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Machine learning for composite materials
Chun-Teh Chen and Grace X Gu · 2019
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Deep learning predicts path-dependent plasticity
M Mozaffar, R Bostanabad, W Chen, K Ehmann, Jian Cao, and MA Bessa · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Machine learning in additive manufacturing: State-of-the-art and perspectives
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Deep learning for topology optimization of 2d metamaterials
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A deep energy method for finite deformation hyperelasticity
Vien Minh Nguyen-Thanh, Xiaoying Zhuang, and Timon Rabczuk · 2020
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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
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Prediction of sorption processes using the deep learning methods (long short-term memory)
Dorian Skrobek, Jaroslaw Krzywanski, Marcin Sosnowski, Anna Kulakowska, Anna Zylka, Karolina Grabowska, Katarzyna Ciesielska, and Wojciech Nowak · 2020
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A loosely-coupled deep reinforcement learning approach for order acceptance decision of mass-individualized printed circuit board manufacturing in industry 4.0
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Dynamic behaviors of bio-inspired structures: Design, mechanisms, and models
Wen Zhang, Jun Xu, and TX Yu · 2022
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A deep learning energy method for hyperelasticity and viscoelasticity
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The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
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A physics-informed variational deeponet for predicting crack path in quasi-brittle materials
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Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model
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SIMULIA · 2020
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Multiphysics modeling of continuous casting of stainless steel
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Surrogate modeling of a nonlinear, biphasic model of articular cartilage with artificial neural networks
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A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
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Flow over an espresso cup: inferring 3-d velocity and pressure fields from tomographic background oriented schlieren via physics-informed neural networks
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Learning the solution operator of parametric partial differential equations with physics-informed deeponets
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Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems
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Implementation of deep learning methods in prediction of adsorption processes
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