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Scientific Machine Learning (SciML) has advanced recently across many different areas in computational science and engineering.
Dendral and meta-dendral: Their applications dimension
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High-re solutions for incompressible flow using the navier-stokes equations and a multigrid method
Urmila Ghia, Kirti N. Ghia, and C. T. Shin · 1982
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Allen Ward, Jeffrey K Liker, John J Cristiano, and Durward K Sobek II · 1995
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Engineering design: a systematic approach
W Beitz, G Pahl, and K Grote · 1996
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Generalized simulated annealing
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Toyota’s principles of set-based concurrent engineering
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Sivam Krish · 2011
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JO Royset, L Bonfiglio, G Vernengo, and S Brizzolara · 2017
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Physics-informed generative adversarial networks for stochastic differential equations, 2018
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Adaptive activation functions in convolutional neural networks
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Roberta: A robustly optimized bert pretraining approach
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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Guofei Pang, Lu Lu, and George Em Karniadakis · 2019
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Variational physics-informed neural networks for solving partial differential equations
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Multidisciplinary risk-adaptive set-based design of supercavitating hydrofoils
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Data augmentation using pre-trained transformer models
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Enrui Zhang, Adar Kahana, Eli Turkel, Rishikesh Ranade, Jay Pathak, and George Em Karniadakis · 2022
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Fractional SEIR model and data-driven predictions of COVID-19 dynamics of omicron variant
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A physics-informed neural network for quantifying the microstructural properties of polycrystalline nickel using ultrasound data: A promising approach for solving inverse problems
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A physics-informed variational deeponet for predicting crack path in quasi-brittle materials
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A set-based approach to dynamic system design using physics informed neural network
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Generative design rationale: Beyond the record and replay paradigm
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Physics-informed neural networks for nonhomogeneous material identification in elasticity imaging, 2020
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Set-based design: a review and new directions
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Fourier neural operator for parametric partial differential equations
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