ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2004.13859
  4. Cited By
A First Principles Approach for Data-Efficient System Identification of
  Spring-Rod Systems via Differentiable Physics Engines

A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines

Conference on Learning for Dynamics & Control (L4DC), 2020
28 April 2020
Kun Wang
Mridul Aanjaneya
Kostas Bekris
    PINN
ArXiv (abs)PDFHTML

Papers citing "A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines"

10 / 10 papers shown
Inferring Dynamic Physical Properties from Video Foundation Models
Inferring Dynamic Physical Properties from Video Foundation Models
Guanqi Zhan
Xianzheng Ma
Weidi Xie
Andrew Zisserman
VGenAI4CE
201
4
0
02 Oct 2025
Physion++: Evaluating Physical Scene Understanding that Requires Online
  Inference of Different Physical Properties
Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical PropertiesNeural Information Processing Systems (NeurIPS), 2023
H. Tung
Mingyu Ding
Zhenfang Chen
Daniel M. Bear
Chuang Gan
J. Tenenbaum
Daniel L. K. Yamins
Judy Fan
Kevin A. Smith
274
33
0
27 Jun 2023
6N-DoF Pose Tracking for Tensegrity Robots
6N-DoF Pose Tracking for Tensegrity Robots
Shiyang Lu
W. R. Johnson
Kun Wang
Xiaonan Huang
Joran W. Booth
Rebecca Kramer‐Bottiglio
Kostas Bekris
360
0
0
29 May 2022
Dojo: A Differentiable Physics Engine for Robotics
Dojo: A Differentiable Physics Engine for Robotics
Taylor A. Howell
Simon Le Cleac'h
Jan Brüdigam
Qianzhong Chen
Mac Schwager
J. Zico Kolter
Mac Schwager
Zachary Manchester
502
50
0
02 Mar 2022
A Recurrent Differentiable Engine for Modeling Tensegrity Robots
  Trainable with Low-Frequency Data
A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data
Kun Wang
Mridul Aanjaneya
Kostas Bekris
311
10
0
28 Feb 2022
Fine-grained differentiable physics: a yarn-level model for fabrics
Fine-grained differentiable physics: a yarn-level model for fabricsInternational Conference on Learning Representations (ICLR), 2022
Deshan Gong
Zhanxing Zhu
A. Bulpitt
He Wang
AI4CE
300
11
0
01 Feb 2022
Parameter Identification and Motion Control for Articulated Rigid Body
  Robots Using Differentiable Position-based Dynamics
Parameter Identification and Motion Control for Articulated Rigid Body Robots Using Differentiable Position-based Dynamics
Fei Liu
Ming Li
Jingpei Lu
Entong Su
Michael C. Yip
AI4CE
224
6
0
15 Jan 2022
Efficient Differentiable Simulation of Articulated Bodies
Efficient Differentiable Simulation of Articulated Bodies
Yi-Ling Qiao
Junbang Liang
V. Koltun
Ming Lin
AI4CE
328
68
0
16 Sep 2021
gradSim: Differentiable simulation for system identification and
  visuomotor control
gradSim: Differentiable simulation for system identification and visuomotor control
Krishna Murthy Jatavallabhula
Lukasz Wawrzyniak
Florian Golemo
Vikram S. Voleti
Petrini
...
Erleben
Liam Paull
Florian Shkurti
Derek Nowrouzezahrai
Sanja Fidler
218
130
0
06 Apr 2021
Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics
  Engine for Tensegrity Robots
Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots
Kun Wang
Mridul Aanjaneya
Kostas Bekris
425
22
0
10 Nov 2020
1
Page 1 of 1