Nathaniel Trask

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Ravi G PatelScientist, Sandia National LabsAdresă de e-mail confirmată pe sandia.gov
Pavel BochevDistinguished Member of technical Staff, Sandia National LaboratoriesAdresă de e-mail confirmată pe sandia.gov
Eric C. CyrComputational Mathematics Department, Sandia National LaboratoriesAdresă de e-mail confirmată pe sandia.gov
Paul KuberrySandia National LaboratoriesAdresă de e-mail confirmată pe sandia.gov
Kookjin LeeArizona State UniversityAdresă de e-mail confirmată pe asu.edu
Mamikon GulianBooz Allen HamiltonAdresă de e-mail confirmată pe bah.com
mauro peregoSandia National LaboratoriesAdresă de e-mail confirmată pe sandia.gov
Michael L. ParksOak Ridge National LaboratoryAdresă de e-mail confirmată pe ornl.gov
Xiaozhe HuTufts UniversityAdresă de e-mail confirmată pe tufts.edu
Kyungjoo KimNVIDIAAdresă de e-mail confirmată pe nvidia.com
Mitchell A. WoodSandia National LabsAdresă de e-mail confirmată pe sandia.gov
Andy HuangSandia National LaboratoriesAdresă de e-mail confirmată pe sandia.gov
Yue YuProfessor of Applied Mathematics, Lehigh UniversityAdresă de e-mail confirmată pe lehigh.edu
David SchmidtUniversity of MassachusettsAdresă de e-mail confirmată pe acad.umass.edu
Paul J. AtzbergerProfessor of Mathematics and Mechanical Engineering, University of California Santa BarbaraAdresă de e-mail confirmată pe ucsb.edu
Marta D'EliaAdjunct Professor, Stanford - Director of AI and ModSim, Atomic MachinesAdresă de e-mail confirmată pe stanford.edu
Huaiqian YouLehigh UniversityAdresă de e-mail confirmată pe lehigh.edu
Martin R. MaxeyBrown UniversityAdresă de e-mail confirmată pe brown.edu
Alexandre TartakovskyUniversity of Illinois Urbana-Champaign and Pacific Northwest National LaboratoryAdresă de e-mail confirmată pe pnnl.gov
Ben J. GrossPost-Doctoral Researcher UCSD Health, DBMIAdresă de e-mail confirmată pe health.ucsd.edu

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Nathaniel Trask

University of Pennsylvania

Adresă de e-mail confirmată pe seas.upenn.edu

Scientific machine learning physics compatible discretization meshless methods multiphysics


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Intercomparison of 3D pore-scale flow and solute transport simulation methods X Yang, Y Mehmani, WA Perkins, A Pasquali, M Schönherr, K Kim, ... Advances in water resources 95, 176-189, 2016	169	2016
Thermodynamically consistent physics-informed neural networks for hyperbolic systems RG Patel, I Manickam, NA Trask, MA Wood, M Lee, I Tomas, EC Cyr Journal of Computational Physics 449, 110754, 2022	157	2022
Smoothed particle hydrodynamics and its applications for multiphase flow and reactive transport in porous media AM Tartakovsky, N Trask, K Pan, B Jones, W Pan, JR Williams Computational Geosciences 20, 807-834, 2016	133	2016
Multidimensional modeling of condensing two-phase ejector flow M Colarossi, N Trask, DP Schmidt, MJ Bergander International journal of refrigeration 35 (2), 290-299, 2012	121	2012
A physics-informed operator regression framework for extracting data-driven continuum models RG Patel, NA Trask, MA Wood, EC Cyr Computer Methods in Applied Mechanics and Engineering 373, 113500, 2021	118	2021
Data-driven learning of nonlocal physics from high-fidelity synthetic data H You, Y Yu, N Trask, M Gulian, M D’Elia Computer Methods in Applied Mechanics and Engineering 374, 113553, 2021	95	2021
An asymptotically compatible meshfree quadrature rule for non-local problems with applications to peridynamics N Trask, H You, Y Yu, M Parks arxiv 1801.04488: preprint submitted to CMAME, 2018	93	2018
Robust training and initialization of deep neural networks: An adaptive basis viewpoint EC Cyr, MA Gulian, RG Patel, M Perego, NA Trask Mathematical and Scientific Machine Learning, 512-536, 2020	90	2020
Diesel spray CFD simulations based on the Σ-Υ Eulerian atomization model JM Garcia-Oliver, JM Pastor, A Pandal, N Trask, E Baldwin, DP Schmidt Atomization and Sprays 23 (1), 2013	88	2013
GMLS-Nets: A framework for learning from unstructured data N Trask, RG Patel, BJ Gross, PJ Atzberger arXiv preprint arXiv:1909.05371, 2019	56	2019
A scalable consistent second-order SPH solver for unsteady low Reynolds number flows N Trask, M Maxey, K Kim, M Perego, ML Parks, K Yang, J Xu Computer Methods in Applied Mechanics and Engineering 289, 155-178, 2015	56	2015
Meshfree methods on manifolds for hydrodynamic flows on curved surfaces: A Generalized Moving Least-Squares (GMLS) approach BJ Gross, N Trask, P Kuberry, PJ Atzberger Journal of Computational Physics 409, 109340, 2020	54	2020
A high-order staggered meshless method for elliptic problems N Trask, M Perego, P Bochev SIAM Journal on Scientific Computing 39 (2), A479-A502, 2017	53	2017
Machine learning structure preserving brackets for forecasting irreversible processes K Lee, N Trask, P Stinis Advances in Neural Information Processing Systems 34, 5696-5707, 2021	51	2021
Partition of unity networks: deep hp-approximation K Lee, NA Trask, RG Patel, MA Gulian, EC Cyr arXiv preprint arXiv:2101.11256, 2021	47	2021
Scalable algorithms for physics-informed neural and graph networks K Shukla, M Xu, N Trask, GE Karniadakis Data-Centric Engineering 3, e24, 2022	45	2022
Compressible modeling of the internal two-phase flow in a gas-centered swirl coaxial fuel injector N Trask, DP Schmidt, M Lightfoot, S Danczyk Journal of Propulsion and Power 28 (4), 685-693, 2012	45	2012
An asymptotically compatible approach for Neumann-type boundary condition on nonlocal problems H You, XY Lu, N Task, Y Yu ESAIM: Mathematical Modelling and Numerical Analysis 54 (4), 1373-1413, 2020	38	2020
Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs N Trask, A Huang, X Hu Journal of Computational Physics 456, 110969, 2022	37	2022
Asymptotically compatible reproducing kernel collocation and meshfree integration for nonlocal diffusion Y Leng, X Tian, N Trask, JT Foster SIAM Journal on Numerical Analysis 59 (1), 88-118, 2021	37	2021

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