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Learning emergent partial differential equations in a learned emergent space

Nature communications, 2022-06, Vol.13 (1), p.3318-3318, Article 3318

2022

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Emergent Spaces for Coupled Oscillators

Frontiers in computational neuroscience, 2020-05, Vol.14, p.36-36

2020

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An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning

IEEE access, 2018, Vol.6, p.77402-77413

2018

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On learning Hamiltonian systems from data

Chaos (Woodbury, N.Y.), 2019-12, Vol.29 (12)

2019

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Coarse-Grained Descriptions of Dynamics for Networks with Both Intrinsic and Structural Heterogeneities

Frontiers in computational neuroscience, 2017-06, Vol.11, p.43-43

2017

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Transformations establishing equivalence across neural networks: When have two networks learned the same task?

Chaos (Woodbury, N.Y.), 2024-07, Vol.34 (7)

2024

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OpenMG: A new multigrid implementation in Python

Numerical linear algebra with applications, 2014-10, Vol.21 (5), p.685-700

2014

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Data-driven and physics informed modeling of Chinese Hamster Ovary cell bioreactors

Computers & chemical engineering, 2024-04, Vol.183 (C), p.108594, Article 108594

2024

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Personalized Algorithm Generation: A Case Study in Learning ODE Integrators

SIAM journal on scientific computing, 2022-01, Vol.44 (4), p.A1911-A1933

2022

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Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information

Computers & chemical engineering, 2023-10, Vol.178, p.108343, Article 108343

2023

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Implementation and (Inverse Modified) Error Analysis for Implicitly Templated ODE-Nets

SIAM journal on applied dynamical systems, 2024-12, Vol.23 (4), p.2643-2669

2024

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Local conformal autoencoder for standardized data coordinates

Proceedings of the National Academy of Sciences - PNAS, 2020-12, Vol.117 (49), p.30918-30927

2020

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Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning

Chaos (Woodbury, N.Y.), 2023-02, Vol.33 (2)

2023

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Global and local reduced models for interacting, heterogeneous agents

Chaos (Woodbury, N.Y.), 2021-07, Vol.31 (7)

2021

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Initializing LSTM internal states via manifold learning

Chaos (Woodbury, N.Y.), 2021-09, Vol.31 (9)

2021

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Dimension Reduction for Heterogeneous Populations of Oscillators

Bertalan, Tom S

2018

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OpenMG: A new multigrid implementation in Python: MULTIGRID IMPLIMENTATION

Numerical linear algebra with applications, 2014-10, Vol.21 (5), p.685-700

2014

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Coarse-grained and Emergent Distributed Parameter Systems from Data

2021 American Control Conference (ACC), 2021, p.4063-4068

2021

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Reduction of breathing artifacts in multifrequency magnetic resonance elastography of the abdomen

Magnetic resonance in medicine, 2021-04, Vol.85 (4), p.1962-1973

2021

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Inversion‐recovery MR elastography of the human brain for improved stiffness quantification near fluid–solid boundaries

Magnetic resonance in medicine, 2021-11, Vol.86 (5), p.2552-2561

2021

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In vivo characterization of brain tumor biomechanics: magnetic resonance elastography in intracranial B16 melanoma and GL261 glioma mouse models

Frontiers in oncology, 2024-09, Vol.14, p.1402578

2024

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Cortical matrix remodeling as a hallmark of relapsing–remitting neuroinflammation in MR elastography and quantitative MRI

Acta neuropathologica, 2024-06, Vol.147 (1), p.8, Article 8

2024

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Microscopic multifrequency MR elastography for mapping viscoelasticity in zebrafish

Magnetic resonance in medicine, 2022-03, Vol.87 (3), p.1435-1445

2022

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Learning emergent partial differential equations in a learned emergent space

Emergent Spaces for Coupled Oscillators

An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning

On learning Hamiltonian systems from data

Coarse-Grained Descriptions of Dynamics for Networks with Both Intrinsic and Structural Heterogeneities

Transformations establishing equivalence across neural networks: When have two networks learned the same task?

OpenMG: A new multigrid implementation in Python

Data-driven and physics informed modeling of Chinese Hamster Ovary cell bioreactors

Personalized Algorithm Generation: A Case Study in Learning ODE Integrators

Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information

Implementation and (Inverse Modified) Error Analysis for Implicitly Templated ODE-Nets

Local conformal autoencoder for standardized data coordinates

Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning

Global and local reduced models for interacting, heterogeneous agents

Initializing LSTM internal states via manifold learning

Dimension Reduction for Heterogeneous Populations of Oscillators

OpenMG: A new multigrid implementation in Python: MULTIGRID IMPLIMENTATION

Coarse-grained and Emergent Distributed Parameter Systems from Data

Reduction of breathing artifacts in multifrequency magnetic resonance elastography of the abdomen

Inversion‐recovery MR elastography of the human brain for improved stiffness quantification near fluid–solid boundaries

In vivo characterization of brain tumor biomechanics: magnetic resonance elastography in intracranial B16 melanoma and GL261 glioma mouse models

Cortical matrix remodeling as a hallmark of relapsing–remitting neuroinflammation in MR elastography and quantitative MRI

Microscopic multifrequency MR elastography for mapping viscoelasticity in zebrafish

Exploring Predictors of Response to Dacomitinib in EGFR -Amplified Recurrent Glioblastoma

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