ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2203.11363
  4. Cited By
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic
  differential equations

PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations

21 March 2022
Weiheng Zhong
Hadi Meidani
    DRL
ArXivPDFHTML

Papers citing "PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations"

11 / 11 papers shown
Title
Four Principles for Physically Interpretable World Models
Jordan Peper
Zhenjiang Mao
Yuang Geng
Siyuan Pan
Ivan Ruchkin
110
1
0
04 Mar 2025
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
Alex Glyn-Davies
A. Vadeboncoeur
O. Deniz Akyildiz
Ieva Kazlauskaite
Mark Girolami
PINN
67
0
0
10 Sep 2024
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing
  Equations
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations
Mozes Jacobs
Bingni W. Brunton
Steven L. Brunton
J. Nathan Kutz
Ryan V. Raut
11
8
0
07 Oct 2023
Physics informed Neural Networks applied to the description of
  wave-particle resonance in kinetic simulations of fusion plasmas
Physics informed Neural Networks applied to the description of wave-particle resonance in kinetic simulations of fusion plasmas
J. Kumar
D. Zarzoso
V. Grandgirard
Jana Ebert
Stefan Kesselheim
PINN
14
0
0
23 Aug 2023
InVAErt networks: a data-driven framework for model synthesis and
  identifiability analysis
InVAErt networks: a data-driven framework for model synthesis and identifiability analysis
Guoxiang Grayson Tong
Carlos A. Sing Long
Daniele E. Schiavazzi
25
7
0
24 Jul 2023
PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial
  Networks for Stochastic Differential Equations
PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations
R. Gao
Yufeng Wang
Min Yang
Chuanjun Chen
GAN
34
2
0
21 Jul 2023
PartitionVAE -- a human-interpretable VAE
PartitionVAE -- a human-interpretable VAE
Fareed Sheriff
S. Pai
DRL
14
0
0
04 Feb 2023
Random Grid Neural Processes for Parametric Partial Differential
  Equations
Random Grid Neural Processes for Parametric Partial Differential Equations
A. Vadeboncoeur
Ieva Kazlauskaite
Y. Papandreou
F. Cirak
Mark Girolami
Ömer Deniz Akyildiz
AI4CE
20
11
0
26 Jan 2023
L-HYDRA: Multi-Head Physics-Informed Neural Networks
L-HYDRA: Multi-Head Physics-Informed Neural Networks
Zongren Zou
George Karniadakis
AI4CE
18
26
0
05 Jan 2023
Fully probabilistic deep models for forward and inverse problems in
  parametric PDEs
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
A. Vadeboncoeur
Ömer Deniz Akyildiz
Ieva Kazlauskaite
Mark Girolami
F. Cirak
AI4CE
23
17
0
09 Aug 2022
Disentangled Generative Models for Robust Prediction of System Dynamics
Disentangled Generative Models for Robust Prediction of System Dynamics
Stathi Fotiadis
Mario Lino Valencia
Shunlong Hu
Stef Garasto
C. Cantwell
Anil Anthony Bharath
DRL
OOD
CML
8
9
0
26 Aug 2021
1