Machine learning of linear differential equations using Gaussian processes von Raissi, Maziar, Perdikaris, Paris, Karniadakis, George Em

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Inferring solutions of differential equations using noisy multi-fidelity data von Raissi, Maziar, Perdikaris, Paris, Karniadakis, George Em

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Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations von Raissi, Maziar, Perdikaris, Paris, Karniadakis, George Em

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Physics-Informed Neural Networks for Heat Transfer Problems von Cai, Shengze, Wang, Zhicheng, Wang, Sifan, Perdikaris, Paris, Karniadakis, George Em

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Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks von Kissas, Georgios, Yang, Yibo, Hwuang, Eileen, Witschey, Walter R., Detre, John A., Perdikaris, Paris

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Composite bayesian optimization in function spaces ising NEON—Neural Epistemic Operator Networks von Guilhoto, Leonardo Ferreira, Perdikaris, Paris

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Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences von Alber, Mark, Buganza Tepole, Adrian, Cannon, William R., De, Suvranu, Dura-Bernal, Salvador, Garikipati, Krishna, Karniadakis, George, Lytton, William W., Perdikaris, Paris, Petzold, Linda, Kuhl, Ellen

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Fractional-Order Viscoelasticity in One-Dimensional Blood Flow Models von Perdikaris, Paris, Karniadakis, George Em

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Physics-Informed Neural Networks for Cardiac Activation Mapping von Sahli Costabal, Francisco, Yang, Yibo, Perdikaris, Paris, Hurtado, Daniel E., Kuhl, Ellen

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Machine Learning of Space-Fractional Differential Equations von Gulian, Mamikon, Raissi, Maziar, Perdikaris, Paris, Karniadakis, George

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Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks von Wang, Sifan, Teng, Yujun, Perdikaris, Paris

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Multiscale Modeling Meets Machine Learning: What Can We Learn? von Peng, Grace C. Y., Alber, Mark, Buganza Tepole, Adrian, Cannon, William R., De, Suvranu, Dura-Bernal, Savador, Garikipati, Krishna, Karniadakis, George, Lytton, William W., Perdikaris, Paris, Petzold, Linda, Kuhl, Ellen

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Adaptive behavior and different thermal experiences of real people: A Bayesian neural network approach to thermal preference prediction and classification von Ma, Nan, Chen, Liang, Hu, Jian, Perdikaris, Paris, Braham, William W.

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Discovering variable fractional orders of advection–dispersion equations from field data using multi-fidelity Bayesian optimization von Pang, Guofei, Perdikaris, Paris, Cai, Wei, Karniadakis, George Em

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Adversarial uncertainty quantification in physics-informed neural networks von Yang, Yibo, Perdikaris, Paris

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Multiscale modeling and simulation of brain blood flow von Perdikaris, Paris, Grinberg, Leopold, Karniadakis, George Em

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Long-time integration of parametric evolution equations with physics-informed DeepONets von Wang, Sifan, Perdikaris, Paris

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Deep learning of free boundary and Stefan problems von Wang, Sifan, Perdikaris, Paris

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Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk–Planet Systems von Mao, Shunyuan, Dong, Ruobing, Yi, Kwang Moo, Lu, Lu, Wang, Sifan, Perdikaris, Paris

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Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration von Sarkar, Soumalya, Mondal, Sudeepta, Joly, Michael, Lynch, Matthew E, Bopardikar, Shaunak D, Acharya, Ranadip, Perdikaris, Paris

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