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. 2102.13156
  4. Cited By
Physics-Integrated Variational Autoencoders for Robust and Interpretable
  Generative Modeling

Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling

25 February 2021
Naoya Takeishi
Alexandros Kalousis
    DRL
    AI4CE
ArXivPDFHTML

Papers citing "Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling"

14 / 14 papers shown
Title
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
31
0
0
02 Oct 2024
Generating Physical Dynamics under Priors
Generating Physical Dynamics under Priors
Zihan Zhou
Xiaoxue Wang
Tianshu Yu
DiffM
AI4CE
50
0
0
01 Sep 2024
Learning Hybrid Dynamics Models With Simulator-Informed Latent States
Learning Hybrid Dynamics Models With Simulator-Informed Latent States
K. Ensinger
Sebastian Ziesche
Sebastian Trimpe
19
1
0
06 Sep 2023
Physics-Informed Computer Vision: A Review and Perspectives
Physics-Informed Computer Vision: A Review and Perspectives
C. Banerjee
Kien Nguyen
Clinton Fookes
G. Karniadakis
PINN
AI4CE
30
27
0
29 May 2023
Knowledge-augmented Deep Learning and Its Applications: A Survey
Knowledge-augmented Deep Learning and Its Applications: A Survey
Zijun Cui
Tian Gao
Kartik Talamadupula
Qiang Ji
12
17
0
30 Nov 2022
Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward
  Trustworthy Estimation of Theory-Driven Models
Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models
Naoya Takeishi
Alexandros Kalousis
AAML
8
3
0
24 Oct 2022
Neural Implicit Representations for Physical Parameter Inference from a
  Single Video
Neural Implicit Representations for Physical Parameter Inference from a Single Video
Florian Hofherr
Lukas Koestler
Florian Bernard
Daniel Cremers
AI4CE
27
9
0
29 Apr 2022
Embedded-model flows: Combining the inductive biases of model-free deep
  learning and explicit probabilistic modeling
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
Gianluigi Silvestri
Emily Fertig
David A. Moore
L. Ambrogioni
BDL
TPM
AI4CE
23
3
0
12 Oct 2021
Neural Networks with Physics-Informed Architectures and Constraints for
  Dynamical Systems Modeling
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
Franck Djeumou
Cyrus Neary
Eric Goubault
S. Putot
Ufuk Topcu
PINN
AI4CE
32
67
0
14 Sep 2021
Physics guided machine learning using simplified theories
Physics guided machine learning using simplified theories
Suraj Pawar
Omer San
Burak Aksoylu
Adil Rasheed
T. Kvamsdal
PINN
AI4CE
92
87
0
18 Dec 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and
  Inverse PDE Problems with Noisy Data
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
Liu Yang
Xuhui Meng
George Karniadakis
PINN
170
755
0
13 Mar 2020
Lagrangian Neural Networks
Lagrangian Neural Networks
M. Cranmer
S. Greydanus
Stephan Hoyer
Peter W. Battaglia
D. Spergel
S. Ho
PINN
121
419
0
10 Mar 2020
Disentangling Physical Dynamics from Unknown Factors for Unsupervised
  Video Prediction
Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction
Vincent Le Guen
Nicolas Thome
AI4CE
PINN
78
287
0
03 Mar 2020
Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems
Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems
F. Lanusse
Peter Melchior
Fred Moolekamp
BDL
12
12
0
09 Dec 2019
1