Zobrazeno 1 - 10
of 1 033
pro vyhledávání: '"PATEL, Ravi P."'
Composite materials with different microstructural material symmetries are common in engineering applications where grain structure, alloying and particle/fiber packing are optimized via controlled manufacturing. In fact these microstructural tunings
Externí odkaz:
http://arxiv.org/abs/2404.17584
The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural network models have proved to be adept at modeling processes with spatial-temporal complexity. Nevertheless, these h
Externí odkaz:
http://arxiv.org/abs/2402.11179
Autor:
Patel, Ravi, Brayne, Angus, Hintzen, Rogier, Jaroslawicz, Daniel, Neculae, Georgiana, Corneil, Dane
Language models hold incredible promise for enabling scientific discovery by synthesizing massive research corpora. Many complex scientific research questions have multiple plausible answers, each supported by evidence of varying strength. However, e
Externí odkaz:
http://arxiv.org/abs/2402.04068
Autor:
Rafiei, Alireza, Moore, Ronald, Choudhary, Tilendra, Marshall, Curtis, Smith, Geoffrey, Roback, John D., Patel, Ravi M., Josephson, Cassandra D., Kamaleswaran, Rishikesan
Objective: Blood transfusions, crucial in managing anemia and coagulopathy in ICU settings, require accurate prediction for effective resource allocation and patient risk assessment. However, existing clinical decision support systems have primarily
Externí odkaz:
http://arxiv.org/abs/2401.00972
Deep operator learning has emerged as a promising tool for reduced-order modelling and PDE model discovery. Leveraging the expressive power of deep neural networks, especially in high dimensions, such methods learn the mapping between functional stat
Externí odkaz:
http://arxiv.org/abs/2204.10909
Projection-based reduced order models are effective at approximating parameter-dependent differential equations that are parametrically separable. When parametric separability is not satisfied, which occurs in both linear and nonlinear problems, proj
Externí odkaz:
http://arxiv.org/abs/2110.10775
Approximation theorists have established best-in-class optimal approximation rates of deep neural networks by utilizing their ability to simultaneously emulate partitions of unity and monomials. Motivated by this, we propose partition of unity networ
Externí odkaz:
http://arxiv.org/abs/2101.11256
Autor:
Patel, Ravi G., Manickam, Indu, Trask, Nathaniel A., Wood, Mitchell A., Lee, Myoungkyu, Tomas, Ignacio, Cyr, Eric C.
Physics-informed neural network architectures have emerged as a powerful tool for developing flexible PDE solvers which easily assimilate data, but face challenges related to the PDE discretization underpinning them. By instead adapting a least squar
Externí odkaz:
http://arxiv.org/abs/2012.05343
The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust. We present here a framework for discovering continuum model
Externí odkaz:
http://arxiv.org/abs/2009.11992
Second-order optimizers hold intriguing potential for deep learning, but suffer from increased cost and sensitivity to the non-convexity of the loss surface as compared to gradient-based approaches. We introduce a coordinate descent method to train d
Externí odkaz:
http://arxiv.org/abs/2006.10123