default search action
Nathaniel Trask
Person information
- affiliation: Sandia National Laboratories, Albuquerque, NM, USA
- affiliation: Brown University, Division of Applied Mathematics, Providence, RI, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
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 - [i36]Anthony Gruber, Kookjin Lee, Haksoo Lim, Noseong Park, Nathaniel Trask:
Efficiently Parameterized Neural Metriplectic Systems. CoRR abs/2405.16305 (2024) - [i35]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) - [i34]Jonas A. Actor, Anthony Gruber, Eric C. Cyr, Nathaniel Trask:
Gaussian Variational Schemes on Bounded and Unbounded Domains. CoRR abs/2410.06219 (2024) - 2023
- [j10]James H. Adler
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
Thermodynamically consistent physics-informed neural networks for hyperbolic systems. J. Comput. Phys. 449: 110754 (2022) - [j8]Nathaniel Trask, Andy Huang
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
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
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
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
A conservative, consistent, and scalable meshfree mimetic method. J. Comput. Phys. 409: 109187 (2020) - [j5]Ben J. Gross, Nathaniel Trask, Paul Kuberry
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
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
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
Compact moving least squares: An optimization framework for generating high-order compact meshless discretizations. J. Comput. Phys. 326: 596-611 (2016)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-19 20:45 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: