Nathaniel Trask

Orcid: 0000-0003-1575-6380

Affiliations:
  • Sandia National Laboratories, Albuquerque, NM, USA
  • Brown University, Division of Applied Mathematics, Providence, RI, USA


According to our database1, Nathaniel Trask authored at least 44 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Data-driven Whitney forms for structure-preserving control volume analysis.
J. Comput. Phys., January, 2024

2023
A Stable Mimetic Finite-Difference Method for Convection-Dominated Diffusion Equations.
SIAM J. Sci. Comput., June, 2023

Graph Convolutions Enrich the Self-Attention in Transformers!
CoRR, 2023

Causal disentanglement of multimodal data.
CoRR, 2023

Reversible and irreversible bracket-based dynamics for deep graph neural networks.
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

Probabilistic partition of unity networks for high-dimensional regression problems.
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

Scalable algorithms for physics-informed neural and graph networks.
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

Polynomial-Spline Networks with Exact Integrals and Convergence Rates.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2022

Hierarchical partition of unity networks: fast multilevel training.
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

Polynomial-Spline Neural Networks with Exact Integrals.
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

Probabilistic partition of unity networks: clustering based deep approximation.
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

Partition of Unity Networks: Deep HP-Approximation.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021

Greedy Fiedler Spectral Partitioning for Data-driven Discrete Exterior Calculus.
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
A conservative, consistent, and scalable meshfree mimetic method.
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

Data-driven learning of robust nonlocal physics from high-fidelity synthetic data.
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

GMLS-Nets: A Machine Learning Framework for Unstructured Data.
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
GMLS-Nets: A framework for learning from unstructured data.
CoRR, 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
A High-Order Staggered Meshless Method for Elliptic Problems.
SIAM J. Sci. Comput., 2017

2016
Compact moving least squares: An optimization framework for generating high-order compact meshless discretizations.
J. Comput. Phys., 2016


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