Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Modeling and Similitude01:12

Modeling and Similitude

573
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
573
Morphogenesis02:19

Morphogenesis

30.1K
Plant morphogenesis—the development of a plant’s form and structure—involves several overlapping developmental processes, including growth and cell differentiation. Precursor cells differentiate into specific cell types, which are organized into the tissues and organ systems that make up the functional plant.
30.1K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

1.3K
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
1.3K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.2K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.2K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

6.5K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
6.5K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

472
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
472

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Longitudinal Comprehensive Biospecimen and Clinical Data Repository for Cancer Early Detection: The InAdvance Study.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Relationship between the distribution of LEDGF along genes and positions of HIV-1 DNA integration.

mBio·2026
Same author

CShaperApp: Segmenting and analyzing cellular morphologies of the developing <i>Caenorhabditis elegans</i> embryo.

Quantitative biology (Beijing, China)·2026
Same author

An effective method for quantification, visualization, and analysis of 3D cell shape during early embryogenesis.

Quantitative biology (Beijing, China)·2026
Same author

EmbSAM: cell boundary localization and Segment Anything Model for fast images of developing embryos.

Communications biology·2025
Same author

An efficient solution to Hidden Markov Models on trees with coupled branches.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2025
Same journal

When is Enough Enough? A Proposed Termination Point for the Number of Replicates in Computational Simulations.

ArXiv·2026
Same journal

A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection.

ArXiv·2026
Same journal

Spatially Masked Regression Reveals Local and Distributed Predictability in Electrophysiological Recordings.

ArXiv·2026
Same journal

A beam--membrane biomechanical vocal fold model incorporating posturing and glottal conformation.

ArXiv·2026
Same journal

Analyzer-less X-ray Interferometry with Super-Resolution Methods.

ArXiv·2026
Same journal

Maximum Matching Accuracy: An Instance Segmentation Evaluation Metric Utilizing Globally Optimal Matching.

ArXiv·2026
See all related articles
  1. Home
  3. Diffeomorph: Learning To Morph 3d Shapes Using Differentiable Agent-based Simulations.
  1. Home
  3. Diffeomorph: Learning To Morph 3d Shapes Using Differentiable Agent-based Simulations.

Related Experiment Video

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.2K

DiffeoMorph: Learning to Morph 3D Shapes Using Differentiable Agent-Based Simulations.

Seong Ho Pahng, Guoye Guan, Benjamin Fefferman

    Arxiv
    |December 25, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    DiffeoMorph enables agents to collectively form complex 3D shapes using a novel differentiable framework. This approach advances developmental biology, robotics, and multi-agent learning by learning morphogenesis protocols.

    More Related Videos

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
    12:06

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

    Published on: March 3, 2023

    4.6K
    Three-Dimensional Shape Modeling and Analysis of Brain Structures
    05:33

    Three-Dimensional Shape Modeling and Analysis of Brain Structures

    Published on: November 14, 2019

    7.6K

    Related Experiment Videos

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.2K
    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
    12:06

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

    Published on: March 3, 2023

    4.6K
    Three-Dimensional Shape Modeling and Analysis of Brain Structures
    05:33

    Three-Dimensional Shape Modeling and Analysis of Brain Structures

    Published on: November 14, 2019

    7.6K

    Area of Science:

    • Computational Biology
    • Robotics
    • Artificial Intelligence

    Background:

    • Biological systems exhibit complex 3D structures formed by collective agent behavior without central control.
    • Understanding distributed control in morphogenesis is crucial for developmental biology, robotics, and multi-agent learning.

    Purpose of the Study:

    • Introduce DiffeoMorph, a differentiable framework for learning morphogenesis protocols.
    • Enable a population of agents to collectively form a target 3D shape.

    Main Methods:

    • Utilize an attention-based SE(3)-equivariant graph neural network for agent position and state updates.
    • Employ a novel shape-matching loss based on 3D Zernike polynomials for continuous shape comparison.
    • Implement an alignment step with implicit differentiation for SO(3) invariance.

    Main Results:

    • Demonstrate the superiority of the 3D Zernike polynomial loss over standard metrics.
    • Showcase DiffeoMorph's ability to generate diverse 3D shapes from simple to complex morphologies.
    • Validate the framework's effectiveness using minimal spatial cues.

    Conclusions:

    • DiffeoMorph provides an effective end-to-end differentiable framework for learning collective shape formation.
    • The developed shape-matching loss and gradient computation methods are robust and efficient.
    • This work offers a promising approach for designing self-organizing systems in biology and artificial intelligence.