Zobrazeno 1 - 10
of 47
pro vyhledávání: '"De Florio, Mario"'
When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model is accurate. Possible sources include irreducible aleatoric uncertainty
Externí odkaz:
http://arxiv.org/abs/2408.07201
Autor:
Sanchez, Juan A., Reddy, Vishnu, Thirouin, Audrey, Bottke, William F., Kareta, Theodore, De Florio, Mario, Sharkey, Benjamin N. L., Battle, Adam, Cantillo, David C., Pearson, Neil
The study of small ($<$300 m) near-Earth objects (NEOs) is important because they are more closely related than larger objects to the precursors of meteorites that fall on Earth. Collisions of these bodies with Earth are also more frequent. Although
Externí odkaz:
http://arxiv.org/abs/2404.18263
Autor:
Cantillo, David C., Reddy, Vishnu, Battle, Adam, Sharkey, Benjamin N. L., Pearson, Neil C., Campbell, Tanner, Satpathy, Akash, De Florio, Mario, Furfaro, Roberto, Sanchez, Juan
Publikováno v:
Planet. Sci. J. 4 177 (2023)
Carbonaceous chondrites are among the most important meteorite types and have played a vital role in deciphering the origin and evolution of our solar system. They have been linked to low-albedo C-type asteroids, but due to subdued absorption bands,
Externí odkaz:
http://arxiv.org/abs/2401.10377
This paper explores the impact of biologically plausible neuron models on the performance of Spiking Neural Networks (SNNs) for regression tasks. While SNNs are widely recognized for classification tasks, their application to Scientific Machine Learn
Externí odkaz:
http://arxiv.org/abs/2401.00369
Discovering mathematical models that characterize the observed behavior of dynamical systems remains a major challenge, especially for systems in a chaotic regime. The challenge is even greater when the physics underlying such systems is not yet unde
Externí odkaz:
http://arxiv.org/abs/2312.14237
Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. We present a new physics-informed framework for parameter estimation and missing physics identificati
Externí odkaz:
http://arxiv.org/abs/2310.01433
Publikováno v:
In Journal of Computational and Applied Mathematics 15 January 2024 436
Autor:
Ahmadi Daryakenari, Nazanin1 (AUTHOR), De Florio, Mario2 (AUTHOR), Shukla, Khemraj2 (AUTHOR), Karniadakis, George Em2 (AUTHOR) george_karniadakis@brown.edu
Publikováno v:
PLoS Computational Biology. 3/12/2024, Vol. 20 Issue 3, p1-33. 33p.
In this work we apply a novel, accurate, fast, and robust physics-informed neural network framework for data-driven parameters discovery of problems modeled via parametric ordinary differential equations (ODEs) called the Extreme Theory of Functional
Externí odkaz:
http://arxiv.org/abs/2008.05554
Autor:
Schiassi, Enrico, Leake, Carl, De Florio, Mario, Johnston, Hunter, Furfaro, Roberto, Mortari, Daniele
In this work we present a novel, accurate, and robust physics-informed method for solving problems involving parametric differential equations (DEs) called the Extreme Theory of Functional Connections, or X-TFC. The proposed method is a synergy of tw
Externí odkaz:
http://arxiv.org/abs/2005.10632