Article... International Conference on Learning Representations2026
Disentangled Representation Learning for Parametric Partial Differential Equations.
Article in ... International Conference on Learning Representations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
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- Hyperelastic Cardiovascular NN-FE: A Framework for Integrating Nonlinear Finite Element Solvers with Neural Networks for the Hyperelastic Modeling of Cardiac Valve Tissue.Research square · 2026Article
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- Generative AI for efficient statistical computation of fluids.Nature communications · 2026Article
- Toward nonlinear representations with Gaussian-splat manifolds for physics-informed learning.Nature communications · 2026Article
- Geometry-conditioned diffusion generation of Lagrangian particle trajectories in human airways.Research square · 2026Article
- Fast-forward prediction of lattice Boltzmann dynamics with physics-informed neural operators.Nature communications · 2026Article
- Learning missing physics from legacy simulators with alternating neural integrators.Nature communications · 2026Article
- Causal multi-fidelity surrogate forward and inverse models for ICF implosions.Scientific reports · 2026Article
- End-to-end robust system discovery in electrical dynamical systems using scientific machine learning.Communications engineering · 2026Article
- Five views on the foundations of data science.Patterns (New York, N.Y.) · 2026Article
- Snapshot 3D image projection using a diffractive decoder.Light, science & applications · 2026Article
- A compact and efficient physics-informed architecture for reconstructing and predicting complex physical fields from low accuracy sparse data.Research square · 2026Article
- Learning patient-specific spatial biomarker dynamics via operator learning for Alzheimer's disease progression.NPJ systems biology and applications · 2026Article
- FunDiff: diffusion models over function spaces for physics-informed generative modeling.Nature communications · 2026Article
- Learning turbulent flows with generative models for super resolution and sparse flow reconstruction.Nature communications · 2026Article
- Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning.NPJ digital medicine · 2026Article
- A variational framework for residual-based adaptivity in neural PDE solvers and operator learning.NPJ artificial intelligence · 2026Article
- NeuberNet: a neural operator solving elastic-plastic partial differential equations at V-notches from low-fidelity elastic simulations.Communications engineering · 2025Article
- One-shot learning for solution operators of partial differential equations.Nature communications · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Neural operators (NOs) excel at learning mappings between function spaces, serving as efficient forward solution approximators for PDE-governed systems. However, as black-box solvers, they offer limited insight into the underlying physical mechanism, due to the lack of interpretable representations of the physical parameters that drive the system. To tackle this challenge, we propose a new paradigm for learning disentangled representations from NO parameters, thereby effectively solving an inverse problem. Specifically, we introduce DisentangO, a novel hyper-neural operator architecture designed to unveil and disentangle latent physical factors of variation embedded within the black-box neural operator parameters. At the core of DisentangO is a multi-task NO architecture that distills the varying parameters of the governing PDE through a task-wise adaptive layer, alongside a variational autoencoder that disentangles these variations into identifiable latent factors. By learning these disentangled representations, DisentangO not only enhances physical interpretability but also enables more robust generalization across diverse systems. Empirical evaluations across supervised, semi-supervised, and unsupervised learning contexts show that DisentangO effectively extracts meaningful and interpretable latent features, bridging the gap between predictive performance and physical understanding in neural operator frameworks. Our code and data accompanying this paper are available at https://github.com/ningliu-iga/DisentangO.
Identifiers
42583553PMC13460308What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.