Nathaniel Trask
Orcid: 0000-0003-1575-6380Affiliations:
- Sandia National Laboratories, Albuquerque, NM, USA
- Brown University, Division of Applied Mathematics, Providence, RI, USA
According to our database1,
Nathaniel Trask
authored at least 54 papers
between 2016 and 2025.
Collaborative distances:
Collaborative distances:
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Bibliography
2025
Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models.
CoRR, September, 2025
CoRR, September, 2025
Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems.
CoRR, August, 2025
CoRR, August, 2025
A discontinuous piecewise polynomial generalized moving least squares scheme for robust finite element analysis on arbitrary grids.
Eng. Comput., June, 2025
Proceedings of the Thirteenth International Conference on Learning Representations, 2025
2024
J. Comput. Phys., January, 2024
CoRR, 2024
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Proceedings of the Neuro Inspired Computational Elements Conference, 2024
2023
A Stable Mimetic Finite-Difference Method for Convection-Dominated Diffusion Equations.
SIAM J. Sci. Comput., June, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
2022
Enforcing exact physics in scientific machine learning: A data-driven exterior calculus on graphs.
J. Comput. Phys., 2022
Thermodynamically consistent physics-informed neural networks for hyperbolic systems.
J. Comput. Phys., 2022
CoRR, 2022
Parameter-varying neural ordinary differential equations with partition-of-unity networks.
CoRR, 2022
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter.
CoRR, 2022
Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery.
CoRR, 2022
Efficient optimization-based quadrature for variational discretization of nonlocal problems.
CoRR, 2022
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2022
Proceedings of the Mathematical and Scientific Machine Learning, 2022
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling.
Proceedings of the Mathematical and Scientific Machine Learning, 2022
2021
Asymptotically Compatible Reproducing Kernel Collocation and Meshfree Integration for Nonlocal Diffusion.
SIAM J. Numer. Anal., 2021
Entropy stable discontinuous Galerkin methods for the shallow water equations with subcell positivity preservation.
CoRR, 2021
Coupling of IGA and Peridynamics for Air-Blast Fluid-Structure Interaction Using an Immersed Approach.
CoRR, 2021
A General-Purpose, Inelastic, Rotation-Free Kirchhoff-Love Shell Formulation for Peridynamics.
CoRR, 2021
CoRR, 2021
Parallel implementation of a compatible high-order meshless method for the Stokes' equations.
CoRR, 2021
An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture.
CoRR, 2021
Machine learning structure preserving brackets for forecasting irreversible processes.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
A Block Coordinate Descent Optimizer for Classification Problems Exploiting Convexity.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021
2020
J. Comput. Phys., 2020
Meshfree methods on manifolds for hydrodynamic flows on curved surfaces: A Generalized Moving Least-Squares (GMLS) approach.
J. Comput. Phys., 2020
A physics-informed operator regression framework for extracting data-driven continuum models.
CoRR, 2020
CoRR, 2020
A unified, stable and accurate meshfree framework for peridynamic correspondence modeling. Part I: core methods.
CoRR, 2020
Asymptotically compatible reproducing kernel collocation and meshfree integration for the peridynamic Navier equation.
CoRR, 2020
Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint.
Proceedings of Mathematical and Scientific Machine Learning, 2020
Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to, 2020
2019
An Asymptotically Compatible Approach For Neumann-Type Boundary Condition On Nonlocal Problems.
CoRR, 2019
Asymptotically compatible meshfree discretization of state-based peridynamics for linearly elastic composite materials.
CoRR, 2019
Mitigation of the self-force effect in unstructured PIC codes using generalized moving least squares.
Comput. Math. Appl., 2019
Mesh-Hardened Finite Element Analysis Through a Generalized Moving Least-Squares Approximation of Variational Problems.
Proceedings of the Large-Scale Scientific Computing - 12th International Conference, 2019
2018
A compatible high-order meshless method for the Stokes equations with applications to suspension flows.
J. Comput. Phys., 2018
2017
SIAM J. Sci. Comput., 2017
2016
Compact moving least squares: An optimization framework for generating high-order compact meshless discretizations.
J. Comput. Phys., 2016