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ORCIDNathaniel Trask
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- affiliation: Sandia National Laboratories, Albuquerque, NM, USA
- affiliation: Brown University, Division of Applied Mathematics, Providence, RI, USA
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2020 – today
- 2024
[j11]Jonas A. Actor
, Xiaozhe Hu, Andy Huang, Scott A. Roberts, Nathaniel Trask:
Data-driven Whitney forms for structure-preserving control volume analysis. J. Comput. Phys. 496: 112520 (2024)- [c12]Bradley H. Theilman, Qian Zhang, Adar Kahana, Eric C. Cyr, Nathaniel Trask, James B. Aimone, George Em Karniadakis:
Spiking Physics-Informed Neural Networks on Loihi 2. NICE 2024: 1-6 - [i35]Anthony Gruber, Kookjin Lee, Haksoo Lim, Noseong Park, Nathaniel Trask:
Efficiently Parameterized Neural Metriplectic Systems. CoRR abs/2405.16305 (2024) - [i34]Shuai Jiang, Jonas A. Actor, Scott A. Roberts, Nathaniel Trask:
A Structure-Preserving Domain Decomposition Method for Data-Driven Modeling. CoRR abs/2406.05571 (2024) - 2023
- [j10]James H. Adler, Casey Cavanaugh, Xiaozhe Hu, Andy Huang, Nathaniel Trask:
A Stable Mimetic Finite-Difference Method for Convection-Dominated Diffusion Equations. SIAM J. Sci. Comput. 45(3): 2973-3000 (2023) - [c11]Anthony Gruber, Kookjin Lee, Nathaniel Trask:
Reversible and irreversible bracket-based dynamics for deep graph neural networks. NeurIPS 2023 - [i33]Anthony Gruber, Kookjin Lee, Nathaniel Trask:
Reversible and irreversible bracket-based dynamics for deep graph neural networks. CoRR abs/2305.15616 (2023) - [i32]Elise Walker, Jonas A. Actor, Carianne Martinez, Nathaniel Trask:
Causal disentanglement of multimodal data. CoRR abs/2310.18471 (2023) - [i31]Jeongwhan Choi, Hyowon Wi, Jayoung Kim, Yehjin Shin, Kookjin Lee, Nathaniel Trask, Noseong Park:
Graph Convolutions Enrich the Self-Attention in Transformers! CoRR abs/2312.04234 (2023) - 2022
- [j9]Ravi G. Patel, Indu Manickam, Nathaniel A. Trask, Mitchell A. Wood, Myoungkyu Lee, Ignacio Tomas, Eric C. Cyr:
Thermodynamically consistent physics-informed neural networks for hyperbolic systems. J. Comput. Phys. 449: 110754 (2022) - [j8]Nathaniel Trask, Andy Huang, Xiaozhe Hu:
Enforcing exact physics in scientific machine learning: A data-driven exterior calculus on graphs. J. Comput. Phys. 456: 110969 (2022) - [c10]Kookjin Lee, Nathaniel Trask, Panos Stinis:
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling. MSML 2022: 65-80 - [c9]Nathaniel Trask, Amelia Henriksen, Carianne Martinez, Eric C. Cyr:
Hierarchical partition of unity networks: fast multilevel training. MSML 2022: 271-286 - [c8]Jonas A. Actor, Andy Huang, Nathaniel Trask:
Polynomial-Spline Networks with Exact Integrals and Convergence Rates. SSCI 2022: 1156-1163 - [i30]Marco Pasetto, Zhaoxiang Shen, Marta D'Elia, Xiaochuan Tian, Nathaniel Trask, David Kamensky:
Efficient optimization-based quadrature for variational discretization of nonlocal problems. CoRR abs/2201.12391 (2022) - [i29]Nathaniel Trask, Carianne Martinez, Kookjin Lee, Brad Boyce:
Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery. CoRR abs/2202.03242 (2022) - [i28]Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George Em Karniadakis:
Scalable algorithms for physics-informed neural and graph networks. CoRR abs/2205.08332 (2022) - [i27]James H. Adler, Casey Cavanaugh, Xiaozhe Hu, Andy Huang, Nathaniel Trask:
A Stable Mimetic Finite-Difference Method for Convection-Dominated Diffusion Equations. CoRR abs/2208.04169 (2022) - [i26]Rubén Villarreal, Nikolaos N. Vlassis, Nhon N. Phan, Tommie A. Catanach, Reese E. Jones, Nathaniel A. Trask, Sharlotte L. B. Kramer, WaiChing Sun:
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter. CoRR abs/2209.13126 (2022) - [i25]Kookjin Lee, Nathaniel Trask:
Parameter-varying neural ordinary differential equations with partition-of-unity networks. CoRR abs/2210.00368 (2022) - [i24]Tiffany Fan, Nathaniel Trask, Marta D'Elia, Eric Darve:
Probabilistic partition of unity networks for high-dimensional regression problems. CoRR abs/2210.02694 (2022) - 2021
- [j7]Yu Leng, Xiaochuan Tian, Nathaniel Trask, John T. Foster:
Asymptotically Compatible Reproducing Kernel Collocation and Meshfree Integration for Nonlocal Diffusion. SIAM J. Numer. Anal. 59(1): 88-118 (2021) - [c7]Andy Huang, Nathaniel Trask, Christopher Brissette, Xiaozhe Hu:
Greedy Fiedler Spectral Partitioning for Data-driven Discrete Exterior Calculus. AAAI Spring Symposium: MLPS 2021 - [c6]Kookjin Lee, Nathaniel Trask, Ravi G. Patel, Mamikon A. Gulian, Eric C. Cyr:
Partition of Unity Networks: Deep HP-Approximation. AAAI Spring Symposium: MLPS 2021 - [c5]Ravi G. Patel, Nathaniel Trask, Mamikon A. Gulian, Eric C. Cyr:
A Block Coordinate Descent Optimizer for Classification Problems Exploiting Convexity. AAAI Spring Symposium: MLPS 2021 - [c4]Kookjin Lee, Nathaniel Trask, Panos Stinis:
Machine learning structure preserving brackets for forecasting irreversible processes. NeurIPS 2021: 5696-5707 - [i23]Yue Yu, Huaiqian You, Nathaniel Trask:
An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture. CoRR abs/2101.01434 (2021) - [i22]Kookjin Lee, Nathaniel A. Trask, Ravi G. Patel, Mamikon A. Gulian, Eric C. Cyr:
Partition of unity networks: deep hp-approximation. CoRR abs/2101.11256 (2021) - [i21]Quang-Thinh Ha, Paul A. Kuberry, Nathaniel A. Trask, Emily M. Ryan:
Parallel implementation of a compatible high-order meshless method for the Stokes' equations. CoRR abs/2104.14447 (2021) - [i20]Kookjin Lee, Nathaniel A. Trask, Panos Stinis:
Machine learning structure preserving brackets for forecasting irreversible processes. CoRR abs/2106.12619 (2021) - [i19]Nathaniel Trask, Mamikon A. Gulian, Andy Huang, Kookjin Lee:
Probabilistic partition of unity networks: clustering based deep approximation. CoRR abs/2107.03066 (2021) - [i18]Masoud Behzadinasab, Mert Alaydin, Nathaniel Trask, Yuri Bazilevs:
A General-Purpose, Inelastic, Rotation-Free Kirchhoff-Love Shell Formulation for Peridynamics. CoRR abs/2107.13062 (2021) - [i17]Masoud Behzadinasab, Georgios Moutsanidis, Nathaniel Trask, John T. Foster, Yuri Bazilevs:
Coupling of IGA and Peridynamics for Air-Blast Fluid-Structure Interaction Using an Immersed Approach. CoRR abs/2108.11265 (2021) - [i16]Kookjin Lee, Nathaniel Trask, Panos Stinis:
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling. CoRR abs/2109.05364 (2021) - [i15]Jonas A. Actor, Andy Huang, Nathaniel Trask:
Polynomial-Spline Neural Networks with Exact Integrals. CoRR abs/2110.14055 (2021) - [i14]Xinhui Wu, Nathaniel Trask, Jesse Chan:
Entropy stable discontinuous Galerkin methods for the shallow water equations with subcell positivity preservation. CoRR abs/2112.07749 (2021) - 2020
- [j6]Nathaniel Trask, Pavel B. Bochev, Mauro Perego:
A conservative, consistent, and scalable meshfree mimetic method. J. Comput. Phys. 409: 109187 (2020) - [j5]Ben J. Gross, Nathaniel Trask, Paul Kuberry, Paul J. Atzberger:
Meshfree methods on manifolds for hydrodynamic flows on curved surfaces: A Generalized Moving Least-Squares (GMLS) approach. J. Comput. Phys. 409: 109340 (2020) - [c3]Nathaniel Trask, Ravi G. Patel, Paul J. Atzberger, Ben J. Gross:
GMLS-Nets: A Machine Learning Framework for Unstructured Data. AAAI Spring Symposium: MLPS 2020 - [c2]Eric C. Cyr, Mamikon A. Gulian, Ravi G. Patel, Mauro Perego, Nathaniel A. Trask:
Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint. MSML 2020: 512-536 - [i13]Yu Leng, Xiaochuan Tian, Nathaniel A. Trask, John T. Foster:
Asymptotically compatible reproducing kernel collocation and meshfree integration for the peridynamic Navier equation. CoRR abs/2001.00649 (2020) - [i12]Masoud Behzadinasab, Nathaniel Trask, Yuri Bazilevs:
A unified, stable and accurate meshfree framework for peridynamic correspondence modeling. Part I: core methods. CoRR abs/2004.11477 (2020) - [i11]Huaiqian You, Yue Yu, Nathaniel Trask, Mamikon A. Gulian, Marta D'Elia:
Data-driven learning of robust nonlocal physics from high-fidelity synthetic data. CoRR abs/2005.10076 (2020) - [i10]Ravi G. Patel, Nathaniel A. Trask, Mamikon A. Gulian, Eric C. Cyr:
A block coordinate descent optimizer for classification problems exploiting convexity. CoRR abs/2006.10123 (2020) - [i9]Ravi G. Patel, Nathaniel A. Trask, Mitchell A. Wood, Eric C. Cyr:
A physics-informed operator regression framework for extracting data-driven continuum models. CoRR abs/2009.11992 (2020) - [i8]Ravi G. Patel, Indu Manickam, Nathaniel A. Trask, Mitchell A. Wood, Myoungkyu Lee, Ignacio Tomas, Eric C. Cyr:
Thermodynamically consistent physics-informed neural networks for hyperbolic systems. CoRR abs/2012.05343 (2020) - [i7]Nathaniel Trask, Andy Huang, Xiaozhe Hu:
Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs. CoRR abs/2012.11799 (2020)
2010 – 2019
- 2019
- [j4]Nathaniel Trask, Pavel B. Bochev, Mauro Perego:
Mitigation of the self-force effect in unstructured PIC codes using generalized moving least squares. Comput. Math. Appl. 78(2): 688-705 (2019) - [c1]Pavel B. Bochev, Nathaniel Trask, Paul Kuberry, Mauro Perego:
Mesh-Hardened Finite Element Analysis Through a Generalized Moving Least-Squares Approximation of Variational Problems. LSSC 2019: 67-75 - [i6]Nathaniel Trask, Benjamin Huntington, David J. Littlewood:
Asymptotically compatible meshfree discretization of state-based peridynamics for linearly elastic composite materials. CoRR abs/1903.00383 (2019) - [i5]Ben J. Gross, Nathaniel Trask, Paul Kuberry, Paul J. Atzberger:
Meshfree Methods on Manifolds for Hydrodynamic Flows on Curved Surfaces: A Generalized Moving Least-Squares (GMLS) Approach. CoRR abs/1905.10469 (2019) - [i4]Yu Leng, Xiaochuan Tian, Nathaniel Trask, John T. Foster:
Asymptotically compatible reproducing kernel collocation and meshfree integration for nonlocal diffusion. CoRR abs/1907.12031 (2019) - [i3]Huaiqian You, Xin Yang Lu, Nathaniel Trask, Yue Yu:
An Asymptotically Compatible Approach For Neumann-Type Boundary Condition On Nonlocal Problems. CoRR abs/1908.03853 (2019) - [i2]Nathaniel Trask, Ravi G. Patel, Ben J. Gross, Paul J. Atzberger:
GMLS-Nets: A framework for learning from unstructured data. CoRR abs/1909.05371 (2019) - [i1]Eric C. Cyr, Mamikon A. Gulian, Ravi G. Patel, Mauro Perego, Nathaniel A. Trask:
Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint. CoRR abs/1912.04862 (2019) - 2018
- [j3]Nathaniel Trask, Martin R. Maxey, Xiaozhe Hu:
A compatible high-order meshless method for the Stokes equations with applications to suspension flows. J. Comput. Phys. 355: 310-326 (2018) - 2017
- [j2]Nathaniel Trask, Mauro Perego, Pavel B. Bochev:
A High-Order Staggered Meshless Method for Elliptic Problems. SIAM J. Sci. Comput. 39(2) (2017) - 2016
- [j1]Nathaniel Trask, Martin R. Maxey, Xiaozhe Hu:
Compact moving least squares: An optimization framework for generating high-order compact meshless discretizations. J. Comput. Phys. 326: 596-611 (2016)
Coauthor Index
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last updated on 2024-10-07 21:17 CEST by the dblp team
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