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Universal Differential Equations for Scientific Machine Learning

Universal Differential Equations for Scientific Machine Learning

13 January 2020
Christopher Rackauckas
Yingbo Ma
Julius Martensen
Collin Warner
K. Zubov
R. Supekar
Dominic J. Skinner
Ali Ramadhan
Alan Edelman
    AI4CE
ArXivPDFHTML

Papers citing "Universal Differential Equations for Scientific Machine Learning"

50 / 75 papers shown
Title
Efficient Training of Physics-enhanced Neural ODEs via Direct Collocation and Nonlinear Programming
Efficient Training of Physics-enhanced Neural ODEs via Direct Collocation and Nonlinear Programming
Linus Langenkamp
Philip Hannebohm
Bernhard Bachmann
36
0
0
06 May 2025
Reliable and Efficient Inverse Analysis using Physics-Informed Neural Networks with Distance Functions and Adaptive Weight Tuning
Reliable and Efficient Inverse Analysis using Physics-Informed Neural Networks with Distance Functions and Adaptive Weight Tuning
Shota Deguchi
Mitsuteru Asai
PINN
AI4CE
81
0
0
25 Apr 2025
Modelling Mean-Field Games with Neural Ordinary Differential Equations
Modelling Mean-Field Games with Neural Ordinary Differential Equations
Anna C. M. Thöni
Yoram Bachrach
Tal Kachman
33
0
0
17 Apr 2025
Optimal Virtual Model Control for Robotics: Design and Tuning of Passivity-Based Controllers
Optimal Virtual Model Control for Robotics: Design and Tuning of Passivity-Based Controllers
Daniel Larby
F. Forni
54
1
0
20 Jan 2025
Understanding and Mitigating Membership Inference Risks of Neural Ordinary Differential Equations
Understanding and Mitigating Membership Inference Risks of Neural Ordinary Differential Equations
Sanghyun Hong
Fan Wu
A. Gruber
Kookjin Lee
42
0
0
12 Jan 2025
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks
Cyrus Neary
Nathan Tsao
Ufuk Topcu
72
1
0
15 Dec 2024
When are dynamical systems learned from time series data statistically
  accurate?
When are dynamical systems learned from time series data statistically accurate?
Jeongjin Park
Nicole Yang
Nisha Chandramoorthy
AI4TS
34
4
0
09 Nov 2024
Efficient, Accurate and Stable Gradients for Neural ODEs
Efficient, Accurate and Stable Gradients for Neural ODEs
Sam McCallum
James Foster
32
4
0
15 Oct 2024
Structural Constraints for Physics-augmented Learning
Structural Constraints for Physics-augmented Learning
Simon Kuang
Xinfan Lin
PINN
26
0
0
07 Oct 2024
Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems
Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems
Alejandro Castañeda Garcia
J. C. V. Gemert
Daan Brinks
Nergis Tömen
38
0
0
02 Oct 2024
Numerically Robust Fixed-Point Smoothing Without State Augmentation
Numerically Robust Fixed-Point Smoothing Without State Augmentation
Nicholas Krämer
31
2
0
30 Sep 2024
Differentiable programming across the PDE and Machine Learning barrier
Differentiable programming across the PDE and Machine Learning barrier
N. Bouziani
David A. Ham
Ado Farsi
PINN
AI4CE
34
1
0
09 Sep 2024
SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems
SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems
Patrick Emami
Zhaonan Li
Saumya Sinha
Truc Nguyen
48
1
0
30 May 2024
Interventionally Consistent Surrogates for Agent-based Simulators
Interventionally Consistent Surrogates for Agent-based Simulators
Joel Dyer
Nicholas Bishop
Yorgos Felekis
Fabio Massimo Zennaro
Anisoara Calinescu
Theodoros Damoulas
Michael Wooldridge
19
6
0
18 Dec 2023
Enhancing Low-Order Discontinuous Galerkin Methods with Neural Ordinary Differential Equations for Compressible Navier--Stokes Equations
Enhancing Low-Order Discontinuous Galerkin Methods with Neural Ordinary Differential Equations for Compressible Navier--Stokes Equations
Shinhoo Kang
Emil M. Constantinescu
AI4CE
22
0
0
29 Oct 2023
Learning Hybrid Dynamics Models With Simulator-Informed Latent States
Learning Hybrid Dynamics Models With Simulator-Informed Latent States
K. Ensinger
Sebastian Ziesche
Sebastian Trimpe
31
1
0
06 Sep 2023
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Alexander Norcliffe
M. Deisenroth
23
3
0
21 Aug 2023
Branched Latent Neural Maps
Branched Latent Neural Maps
M. Salvador
Alison Lesley Marsden
30
4
0
04 Aug 2023
Automatic Differentiation for Inverse Problems with Applications in
  Quantum Transport
Automatic Differentiation for Inverse Problems with Applications in Quantum Transport
I. Williams
E. Polizzi
13
0
0
18 Jul 2023
Trainability, Expressivity and Interpretability in Gated Neural ODEs
Trainability, Expressivity and Interpretability in Gated Neural ODEs
T. Kim
T. Can
K. Krishnamurthy
AI4CE
35
4
0
12 Jul 2023
Neural Astrophysical Wind Models
Neural Astrophysical Wind Models
Dustin D. Nguyen
16
2
0
20 Jun 2023
An analysis of Universal Differential Equations for data-driven
  discovery of Ordinary Differential Equations
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations
Mattia Silvestri
Federico Baldo
Eleonora Misino
M. Lombardi
AI4CE
16
1
0
17 Jun 2023
Learning Dynamical Systems from Noisy Data with Inverse-Explicit
  Integrators
Learning Dynamical Systems from Noisy Data with Inverse-Explicit Integrators
Haakon Noren
Sølve Eidnes
E. Celledoni
21
3
0
06 Jun 2023
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using
  Pumas
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas
Mohamed Tarek
J. Storópoli
Casey B. Davis
C. Elrod
Julius Krumbiegel
Chris Rackauckas
V. Ivaturi
GP
15
3
0
31 Mar 2023
Locally Regularized Neural Differential Equations: Some Black Boxes Were
  Meant to Remain Closed!
Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed!
Avik Pal
Alan Edelman
Chris Rackauckas
22
3
0
03 Mar 2023
Compositional Learning of Dynamical System Models Using Port-Hamiltonian
  Neural Networks
Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks
Cyrus Neary
Ufuk Topcu
PINN
AI4CE
6
12
0
01 Dec 2022
Neural DAEs: Constrained neural networks
Neural DAEs: Constrained neural networks
Tue Boesen
E. Haber
Uri M. Ascher
31
3
0
25 Nov 2022
Neural ODEs as Feedback Policies for Nonlinear Optimal Control
Neural ODEs as Feedback Policies for Nonlinear Optimal Control
I. O. Sandoval
Panagiotis Petsagkourakis
Ehecatl Antonio del Rio Chanona
17
9
0
20 Oct 2022
Homotopy-based training of NeuralODEs for accurate dynamics discovery
Homotopy-based training of NeuralODEs for accurate dynamics discovery
Joon-Hyuk Ko
Hankyul Koh
Nojun Park
W. Jhe
40
8
0
04 Oct 2022
Differentiable Programming for Earth System Modeling
Differentiable Programming for Earth System Modeling
Maximilian Gelbrecht
Alistair J R White
S. Bathiany
Niklas Boers
19
16
0
29 Aug 2022
NeuralUQ: A comprehensive library for uncertainty quantification in
  neural differential equations and operators
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators
Zongren Zou
Xuhui Meng
Apostolos F. Psaros
George Karniadakis
AI4CE
25
35
0
25 Aug 2022
Interpretable Polynomial Neural Ordinary Differential Equations
Interpretable Polynomial Neural Ordinary Differential Equations
Colby Fronk
Linda R. Petzold
27
26
0
09 Aug 2022
Human Trajectory Prediction via Neural Social Physics
Human Trajectory Prediction via Neural Social Physics
Jiangbei Yue
Dinesh Manocha
He-Nan Wang
AI4CE
19
100
0
21 Jul 2022
Automatic differentiation and the optimization of differential equation
  models in biology
Automatic differentiation and the optimization of differential equation models in biology
S. Frank
14
6
0
10 Jul 2022
j-Wave: An open-source differentiable wave simulator
j-Wave: An open-source differentiable wave simulator
A. Stanziola
Simon Arridge
B. Cox
B. Treeby
VLM
33
21
0
30 Jun 2022
E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations
Jongwan Kim
Dongjin Lee
Byunggook Na
Seongsik Park
Jeonghee Jo
Sung-Hoon Yoon
29
0
0
15 Jun 2022
Automated differential equation solver based on the parametric
  approximation optimization
Automated differential equation solver based on the parametric approximation optimization
A. Hvatov
Tatiana Tikhonova
14
4
0
11 May 2022
Machines of finite depth: towards a formalization of neural networks
Machines of finite depth: towards a formalization of neural networks
Pietro Vertechi
M. Bergomi
PINN
16
2
0
27 Apr 2022
Optimizing differential equations to fit data and predict outcomes
Optimizing differential equations to fit data and predict outcomes
S. Frank
24
4
0
16 Apr 2022
Machine Learning and Deep Learning -- A review for Ecologists
Machine Learning and Deep Learning -- A review for Ecologists
Maximilian Pichler
F. Hartig
36
124
0
11 Apr 2022
Monarch: Expressive Structured Matrices for Efficient and Accurate
  Training
Monarch: Expressive Structured Matrices for Efficient and Accurate Training
Tri Dao
Beidi Chen
N. Sohoni
Arjun D Desai
Michael Poli
Jessica Grogan
Alexander Liu
Aniruddh Rao
Atri Rudra
Christopher Ré
22
87
0
01 Apr 2022
Input-to-State Stable Neural Ordinary Differential Equations with
  Applications to Transient Modeling of Circuits
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits
Alan Yang
J. Xiong
Maxim Raginsky
E. Rosenbaum
AI4TS
16
4
0
14 Feb 2022
Trust in AI: Interpretability is not necessary or sufficient, while
  black-box interaction is necessary and sufficient
Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient
Max W. Shen
25
18
0
10 Feb 2022
Physics-informed neural networks for solving parametric magnetostatic
  problems
Physics-informed neural networks for solving parametric magnetostatic problems
Andrés Beltrán-Pulido
Ilias Bilionis
D. Aliprantis
24
34
0
08 Feb 2022
Physics-informed neural networks for non-Newtonian fluid
  thermo-mechanical problems: an application to rubber calendering process
Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process
Thi Nguyen Khoa Nguyen
T. Dairay
Raphael Meunier
Mathilde Mougeot
PINN
AI4CE
70
29
0
31 Jan 2022
Scientific Machine Learning through Physics-Informed Neural Networks:
  Where we are and What's next
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
S. Cuomo
Vincenzo Schiano Di Cola
F. Giampaolo
G. Rozza
Maizar Raissi
F. Piccialli
PINN
26
1,177
0
14 Jan 2022
An Overview of Healthcare Data Analytics With Applications to the
  COVID-19 Pandemic
An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic
Z. Fei
Y. Ryeznik
O. Sverdlov
C. Tan
Weng Kee Wong
21
20
0
25 Nov 2021
NeuralPDE: Modelling Dynamical Systems from Data
NeuralPDE: Modelling Dynamical Systems from Data
Andrzej Dulny
Andreas Hotho
Anna Krause
AI4CE
22
11
0
15 Nov 2021
Physics-enhanced deep surrogates for partial differential equations
Physics-enhanced deep surrogates for partial differential equations
R. Pestourie
Youssef Mroueh
Chris Rackauckas
Payel Das
Steven G. Johnson
PINN
AI4CE
25
15
0
10 Nov 2021
A research framework for writing differentiable PDE discretizations in
  JAX
A research framework for writing differentiable PDE discretizations in JAX
A. Stanziola
Simon Arridge
B. Cox
B. Treeby
24
8
0
09 Nov 2021
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