1.1k citations · 7.2k across the 70 of their papers we have counts for
7 papers · 1 filter
Multiscale modeling meets machine learning: What can we learn?
Grace C. Y. Peng, Mark Alber, Adrian Buganza Tepole +9
Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incred…
Integrating Machine Learning and Multiscale Modeling: Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences
Mark Alber, Adrian Buganza Tepole, William Cannon +8
Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time…
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models
Francisco Sahli Costabal, Paris Perdikaris, Ellen Kuhl +1
Machine learning techniques typically rely on large datasets to create accurate classifiers. However, there are situations when data is scarce and expensive to acquire. This is the…
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
Georgios Kissas, Yibo Yang, Eileen Hwuang +3
Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and…
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations
Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis +1
We investigate the use of discrete and continuous versions of physics-informed neural network methods for learning unknown dynamics or constitutive relations of a dynamical system.…
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis +1
Surrogate modeling and uncertainty quantification tasks for PDE systems are most often considered as supervised learning problems where input and output data pairs are used for tra…
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
Yibo Yang, Paris Perdikaris
We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variat…