Zobrazeno 1 - 10
of 867
pro vyhledávání: '"SRIVASTAVA, ANKIT"'
The manipulation of mechanical waves is a long-standing challenge for scientists and engineers, as numerous devices require their control. The current forefront of research in the control of classical waves has emerged from a seemingly unrelated fiel
Externí odkaz:
http://arxiv.org/abs/2407.02513
Autor:
Mulukutla, Mrinalini, Person, A. Nicole, Voigt, Sven, Kuettner, Lindsey, Kappes, Branden, Khatamsaz, Danial, Robinson, Robert, Salas, Daniel, Xu, Wenle, Lewis, Daniel, Eoh, Hongkyu, Xiao, Kailu, Wang, Haoren, Saini, Jaskaran Singh, Mahat, Raj, Hastings, Trevor, Skokan, Matthew, Attari, Vahid, Elverud, Michael, Paramore, James D., Butler, Brady, Vecchio, Kenneth, Kalidindi, Surya R., Allaire, Douglas, Karaman, Ibrahim, Thomas, Edwin L., Pharr, George, Srivastava, Ankit, Arróyave, Raymundo
Algorithmic materials discovery is a multi-disciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, computational modeling, and optimization. Central to this effort is a r
Externí odkaz:
http://arxiv.org/abs/2405.13132
Autor:
Hastings, Trevor, Mulukutla, Mrinalini, Khatamsaz, Danial, Salas, Daniel, Xu, Wenle, Lewis, Daniel, Person, Nicole, Skokan, Matthew, Miller, Braden, Paramore, James, Butler, Brady, Allaire, Douglas, Karaman, Ibrahim, Pharr, George, Srivastava, Ankit, Arroyave, Raymundo
In this study, we introduce a groundbreaking framework for materials discovery, we efficiently navigate a vast phase space of material compositions by leveraging Batch Bayesian statistics in order to achieve specific performance objectives. This appr
Externí odkaz:
http://arxiv.org/abs/2405.08900
Autor:
Lopez-Doriga, Barbara, Atzori, Marco, Vinuesa, Ricardo, Bae, H. Jane, Srivastava, Ankit, Dawson, Scott T. M.
This research focuses on the identification and causality analysis of coherent structures that arise in turbulent flows in square and rectangular ducts. Coherent structures are first identified from direct numerical simulation data via proper orthogo
Externí odkaz:
http://arxiv.org/abs/2401.06295
In this paper, we introduce a new approach for soft robot shape formation and morphing using approximate distance fields. The method uses concepts from constructive solid geometry, R-functions, to construct an approximate distance function to the bou
Externí odkaz:
http://arxiv.org/abs/2306.06120
In this paper, we present a powerful method (Atomistic Green's Function, AGF) for calculating the effective Hamiltonian of acoustic and elastic wave-scatterers. The ability to calculate the effective Hamiltonian allows for the study of scattering pro
Externí odkaz:
http://arxiv.org/abs/2301.12259
Autor:
Srivastava, Ankit1 (AUTHOR), Wang, Qinlu2 (AUTHOR), Orrù, Christina D.1 (AUTHOR), Fernandez, Manel3 (AUTHOR), Compta, Yaroslau3 (AUTHOR), Ghetti, Bernardino4 (AUTHOR), Zanusso, Gianluigi5 (AUTHOR), Zou, Wen-Quan6,7 (AUTHOR), Caughey, Byron1 (AUTHOR) bcaughey@nih.gov, Beauchemin, Catherine A. A.8,9 (AUTHOR) bcaughey@nih.gov
Publikováno v:
PLoS Pathogens. 9/20/2024, Vol. 20 Issue 9, p1-24. 24p.
Physics-Informed Neural Networks (PINNs) are a class of deep learning neural networks that learn the response of a physical system without any simulation data, and only by incorporating the governing partial differential equations (PDEs) in their los
Externí odkaz:
http://arxiv.org/abs/2210.14320
Autor:
Martínez-Sánchez, Álvaro, López, Esteban, Clainche, Soledad Le, Lozano-Durán, Adrián, Srivastava, Ankit, Vinuesa, Ricardo
The aim of this work is to analyse the formation mechanisms of large-scale coherent structures in the flow around a wall-mounted square cylinder, due to their impact on pollutant transport within cities. To this end, we assess causal relations betwee
Externí odkaz:
http://arxiv.org/abs/2209.15356