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Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural
  networks: perspectives from the theory of controlled diffusions and measures
  on path space

Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space

11 May 2020
Nikolas Nusken
Lorenz Richter
    AI4CE
ArXivPDFHTML

Papers citing "Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space"

41 / 41 papers shown
Title
SpectR: Dynamically Composing LM Experts with Spectral Routing
SpectR: Dynamically Composing LM Experts with Spectral Routing
William Fleshman
Benjamin Van Durme
MoMe
MoE
65
0
0
04 Apr 2025
Underdamped Diffusion Bridges with Applications to Sampling
Denis Blessing
Julius Berner
Lorenz Richter
Gerhard Neumann
DiffM
39
1
0
02 Mar 2025
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training
Julius Berner
Lorenz Richter
Marcin Sendera
Jarrid Rector-Brooks
Nikolay Malkin
OffRL
65
3
0
10 Jan 2025
Learned Reference-based Diffusion Sampling for multi-modal distributions
Learned Reference-based Diffusion Sampling for multi-modal distributions
Maxence Noble
Louis Grenioux
Marylou Gabrié
Alain Durmus
DiffM
36
2
0
25 Oct 2024
An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
Mengjian Hua
Matthieu Laurière
Eric Vanden-Eijnden
36
3
0
07 Oct 2024
Scalable Simulation-free Entropic Unbalanced Optimal Transport
Scalable Simulation-free Entropic Unbalanced Optimal Transport
Jaemoo Choi
Jaewoong Choi
OT
30
1
0
03 Oct 2024
A Taxonomy of Loss Functions for Stochastic Optimal Control
A Taxonomy of Loss Functions for Stochastic Optimal Control
Carles Domingo-Enrich
37
3
0
01 Oct 2024
Dynamical Measure Transport and Neural PDE Solvers for Sampling
Dynamical Measure Transport and Neural PDE Solvers for Sampling
Jingtong Sun
Julius Berner
Lorenz Richter
Marius Zeinhofer
Johannes Müller
Kamyar Azizzadenesheli
Anima Anandkumar
OT
DiffM
42
9
0
10 Jul 2024
Solving Poisson Equations using Neural Walk-on-Spheres
Solving Poisson Equations using Neural Walk-on-Spheres
Hong Chul Nam
Julius Berner
Anima Anandkumar
37
3
0
05 Jun 2024
Amortizing intractable inference in diffusion models for vision, language, and control
Amortizing intractable inference in diffusion models for vision, language, and control
S. Venkatraman
Moksh Jain
Luca Scimeca
Minsu Kim
Marcin Sendera
...
Alexandre Adam
Jarrid Rector-Brooks
Yoshua Bengio
Glen Berseth
Nikolay Malkin
70
26
0
31 May 2024
Stochastic Optimal Control for Diffusion Bridges in Function Spaces
Stochastic Optimal Control for Diffusion Bridges in Function Spaces
Byoungwoo Park
Jungwon Choi
Sungbin Lim
Juho Lee
55
3
0
31 May 2024
Generative Modelling with Tensor Train approximations of
  Hamilton--Jacobi--Bellman equations
Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations
David Sommer
Robert Gruhlke
Max Kirstein
Martin Eigel
Claudia Schillings
29
3
0
23 Feb 2024
Risk-neutral limit of adaptive importance sampling of random stopping
  times
Risk-neutral limit of adaptive importance sampling of random stopping times
Carsten Hartmann
Annika Joster
11
0
0
13 Feb 2024
Improved off-policy training of diffusion samplers
Improved off-policy training of diffusion samplers
Marcin Sendera
Minsu Kim
Sarthak Mittal
Pablo Lemos
Luca Scimeca
Jarrid Rector-Brooks
Alexandre Adam
Yoshua Bengio
Nikolay Malkin
OffRL
68
18
0
07 Feb 2024
Approximation of Solution Operators for High-dimensional PDEs
Approximation of Solution Operators for High-dimensional PDEs
Nathan Gaby
Xiaojing Ye
30
0
0
18 Jan 2024
Stochastic Optimal Control Matching
Stochastic Optimal Control Matching
Carles Domingo-Enrich
Jiequn Han
Brandon Amos
Joan Bruna
Ricky T. Q. Chen
DiffM
20
6
0
04 Dec 2023
Deep learning probability flows and entropy production rates in active
  matter
Deep learning probability flows and entropy production rates in active matter
Nicholas M. Boffi
Eric Vanden-Eijnden
DiffM
25
17
0
22 Sep 2023
From continuous-time formulations to discretization schemes: tensor
  trains and robust regression for BSDEs and parabolic PDEs
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs
Lorenz Richter
Leon Sallandt
Nikolas Nusken
21
4
0
28 Jul 2023
Transgressing the boundaries: towards a rigorous understanding of deep
  learning and its (non-)robustness
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness
C. Hartmann
Lorenz Richter
AAML
27
2
0
05 Jul 2023
Improved sampling via learned diffusions
Improved sampling via learned diffusions
Lorenz Richter
Julius Berner
DiffM
37
52
0
03 Jul 2023
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
Francisco Vargas
Shreyas Padhy
Denis Blessing
Nikolas Nusken
DiffM
OT
55
3
0
03 Jul 2023
Deep Stochastic Mechanics
Deep Stochastic Mechanics
Elena Orlova
Aleksei Ustimenko
Ruoxi Jiang
Peter Y. Lu
Rebecca Willett
DiffM
49
0
0
31 May 2023
To smooth a cloud or to pin it down: Guarantees and Insights on Score
  Matching in Denoising Diffusion Models
To smooth a cloud or to pin it down: Guarantees and Insights on Score Matching in Denoising Diffusion Models
Francisco Vargas
Teodora Reu
A. Kerekes
Michael M Bronstein
DiffM
35
1
0
16 May 2023
Neural Control of Parametric Solutions for High-dimensional Evolution
  PDEs
Neural Control of Parametric Solutions for High-dimensional Evolution PDEs
Nathan Gaby
X. Ye
Haomin Zhou
19
6
0
31 Jan 2023
An optimal control perspective on diffusion-based generative modeling
An optimal control perspective on diffusion-based generative modeling
Julius Berner
Lorenz Richter
Karen Ullrich
DiffM
44
80
0
02 Nov 2022
Double-Loop Importance Sampling for McKean--Vlasov Stochastic
  Differential Equation
Double-Loop Importance Sampling for McKean--Vlasov Stochastic Differential Equation
Nadhir Ben Rached
A. Haji-Ali
Shyam Mohan Subbiah Pillai
Raúl Tempone
17
3
0
14 Jul 2022
Lagrangian Density Space-Time Deep Neural Network Topology
Lagrangian Density Space-Time Deep Neural Network Topology
B. Bishnoi
PINN
25
1
0
30 Jun 2022
Robust SDE-Based Variational Formulations for Solving Linear PDEs via
  Deep Learning
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning
Lorenz Richter
Julius Berner
27
19
0
21 Jun 2022
Computational Doob's h-transforms for Online Filtering of Discretely
  Observed Diffusions
Computational Doob's h-transforms for Online Filtering of Discretely Observed Diffusions
Nicolas Chopin
Andras Fulop
J. Heng
Alexandre Hoang Thiery
23
1
0
07 Jun 2022
Interpolating between BSDEs and PINNs: deep learning for elliptic and
  parabolic boundary value problems
Interpolating between BSDEs and PINNs: deep learning for elliptic and parabolic boundary value problems
Nikolas Nusken
Lorenz Richter
PINN
DiffM
31
27
0
07 Dec 2021
Bayesian Learning via Neural Schrödinger-Föllmer Flows
Bayesian Learning via Neural Schrödinger-Föllmer Flows
Francisco Vargas
Andrius Ovsianas
David Fernandes
Mark Girolami
Neil D. Lawrence
Nikolas Nusken
BDL
40
45
0
20 Nov 2021
Learning-Based Importance Sampling via Stochastic Optimal Control for
  Stochastic Reaction Networks
Learning-Based Importance Sampling via Stochastic Optimal Control for Stochastic Reaction Networks
Chiheb Ben Hammouda
Nadhir Ben Rached
Raúl Tempone
Sophia Wiechert
11
4
0
27 Oct 2021
Gradient-augmented Supervised Learning of Optimal Feedback Laws Using
  State-dependent Riccati Equations
Gradient-augmented Supervised Learning of Optimal Feedback Laws Using State-dependent Riccati Equations
G. Albi
Sara Bicego
D. Kalise
11
26
0
06 Mar 2021
Stein Variational Gradient Descent: many-particle and long-time
  asymptotics
Stein Variational Gradient Descent: many-particle and long-time asymptotics
Nikolas Nusken
D. M. Renger
27
22
0
25 Feb 2021
Solving high-dimensional parabolic PDEs using the tensor train format
Solving high-dimensional parabolic PDEs using the tensor train format
Lorenz Richter
Leon Sallandt
Nikolas Nusken
14
49
0
23 Feb 2021
Nonasymptotic bounds for suboptimal importance sampling
Nonasymptotic bounds for suboptimal importance sampling
C. Hartmann
Lorenz Richter
12
10
0
18 Feb 2021
An overview on deep learning-based approximation methods for partial
  differential equations
An overview on deep learning-based approximation methods for partial differential equations
C. Beck
Martin Hutzenthaler
Arnulf Jentzen
Benno Kuckuck
30
146
0
22 Dec 2020
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
Lorenz Richter
Ayman Boustati
Nikolas Nusken
Francisco J. R. Ruiz
Ömer Deniz Akyildiz
DRL
138
48
0
20 Oct 2020
Space-time deep neural network approximations for high-dimensional
  partial differential equations
Space-time deep neural network approximations for high-dimensional partial differential equations
F. Hornung
Arnulf Jentzen
Diyora Salimova
AI4CE
29
19
0
03 Jun 2020
Neural networks-based backward scheme for fully nonlinear PDEs
Neural networks-based backward scheme for fully nonlinear PDEs
H. Pham
X. Warin
Maximilien Germain
14
85
0
31 Jul 2019
Variational Inference via $χ$-Upper Bound Minimization
Variational Inference via χχχ-Upper Bound Minimization
Adji Bousso Dieng
Dustin Tran
Rajesh Ranganath
John Paisley
David M. Blei
BDL
81
36
0
01 Nov 2016
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