ICLR 2026 Past Other
AI&PDE: ICLR 2026 Workshop on AI and Partial Differential Equations
AI&PDE
- Submission deadline
- Feb 11, 2026, 23:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (111)
Fetched from OpenReview (v2) on 2026-06-10.
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(U)NFV: (Un)Supervised Neural Finite Volume Methods for Solving Hyperbolic PDEs
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$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks
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3D-PINNS: A UNIFIED FRAMEWORK FOR DIMENSION-WISE INTERPRETABILITY AND ADAPTIVE DOMAIN DECOMPOSITION
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A Conservation Law Perspective on Explainability in Spiking Neural Networks
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A Data-Parallel Additively Preconditioned Trust-Region Strategy for Physics-Informed Neural Networks
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A Multigrid-inspired Neural Iterative Solver for Poisson Equations on Large Voxel Grids
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A Neural Score-Based Method for Deterministic Collisional Plasma Simulation
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AB-PIELMS: ADAPTIVE-BASIS PHYSICS-INFORMED EXTREME LEARNING MACHINES FOR RESIDUAL-DRIVEN DOMAIN DECOMPOSITION
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Accelerating PINN Training via RL-based Adaptive Loss Control
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Adaptive SDE Interpolants for Calibrated Probabilistic PDE Forecasting
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Adaptive Test-Time Compute Allocation for Neural PDE Solvers
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Adaptive Tokenization for Vision Transformer PDE Simulation
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Astral: training physics-informed neural networks with error majorants
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ATTENTION-ENHANCED NEURAL OPERATOR FOR VARIABLE-TIMESTEP PREDICTION OF PDES
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AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing
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Born-Series-Inspired Residual Metric for Learned Preconditioners
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Causal Field Theory: Causal Semantics for PDE-Based Spatio-Temporal Systems
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Chebyshev-Augmented One-Shot Transfer Learning for PINNs on Nonlinear Differential Equations
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CHLU: The Causal Hamiltonian Learning Unit as a Symplectic Primitive for Deep Learning
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COARSERL: A GRAPH REINFORCEMENT LEARNING METHOD FOR ALGEBRAIC MULTIGRID COARSENING
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Compositional Neural Operators for Multi-Dimensional Fluid Dynamics
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Constructing Machine-Precision Neural Networks with Quasi-Interpolants
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Data-Efficient Neural Operator Training via Physics-Based Active Learning
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Decoding Partial Differential Equations: Cross-Modal Adaptation of Decoder-only Models to PDEs
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Decoupled Diffusion Solver for Inverse Problems on Function Spaces
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Deep Learning Based Surrogate Modeling of PDE Governed Systems Using Fourier Neural Operators (FNOs): Application to Clarifier Dynamics in Wastewater Treatment
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Direct Learning of Calibration-Aware Uncertainty for Neural PDE Surrogates
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Discovering Bäcklund Transformations with PDE Foundation Models
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Diversity-Aware Adaptive Collocation for Physics-Informed Neural Networks via Sparse QUBO Optimization and Hybrid Coresets
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ECLIPSE: A Composable Pipeline for Predicting ecDNA Formation, Evolution, and Therapeutic Vulnerabilities in Cancer
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Empirical Stability Analysis of Kolmogorov-Arnold Networks in Hard-Constrained Recurrent Physics-Informed Discovery
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EqGINO: Equivariant Geometry-Informed Fourier Neural Operators for 3D PDEs
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Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks
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Fast Multiscale PDE Solvers via Multilevel Domain Decomposition and Random Features
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Fast, Convex and Conditioned Single-Layer Network for Learning Multi-Fidelity Univariate Data and Linear Differential Equations
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FastLSQ: Solving PDEs in One Shot via Fourier Features with Exact Analytical Derivatives
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Flow-Matching Sampling in Physics-Informed Neural Networks for PDEs with Sharp Source Terms
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Fourier Neural Operators for Geodynamic Modeling: A Hybrid Surrogate–Solver Framework
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From Large-Scale Winds to Urban Decision Making: A Cross-Scale Framework for Wind-Aware UAV Navigation
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From RawTokens to PhysSummary: Probing Text Interfaces for Inverse 1D PDE Parameter Estimation
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Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage
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Generalization Analysis and Improved Shape Representation with Neural Signed Distance Functions
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Gradient Scaling Effects In Adaptive Spectral PINNs For Stiff Nonlinear ODEs
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Green's Neural Operator with Neumann conditions for EMG volume conductor modelling
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HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning
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Intrinsic Green's Learning: Supervised Learning on Manifolds via Inverse PDE
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Kernel-Adaptive Physics-Informed Shallow Meta-Learning for Parametric Linear PDEs
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Kinetic-based regularization: Learning spatial derivatives and PDE applications
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Kraus Constrained Sequence Learning For Quantum Trajectories from Continuous Measurement
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Late Fusion Neural Operators for Parameterized Partial Differential Equations
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Latent Reciprocity Representation: Bidirectional Latent-Space Alignment as Physics-Aware Regularization for Neural Operators
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Learning Dengue Dynamics through Hybrid Equation-Guided and Data-Driven Models
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LEARNING EMBEDDINGS OF NON-LINEAR PDES: THE BURGERS’ EQUATION
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Learning Heat-Based Equations in Self-Similar Variables
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Learning Mesh-Free Discrete Differential Operators with Self-Supervised Graph Neural Networks
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Learning Parameterized Nonlinear Elasticity on Curved Surfaces
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Learning Spatially-Varying Fractional Orders in PDEs
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Learning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEs
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Learning-guided Kansa collocation for forward and inverse PDEs beyond linearity
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Limits of Resolution Equivariance in Fourier Neural Operators
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LLM-Driven Loss Balancing for Physics-Informed Neural Networks
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Momentum-Accelerated Structured Preconditioning for Physics-Informed Neural Networks
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mPOD-DeepONet: POD-DeepONet for Multiple Outputs
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Multi-Trajectory Physics-Informed Neural Networks for HJB Equations with Hard Terminal Constraints: Optimal Execution and High-Dimensional LQR
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Neural Bloch Eigensolver for Honeycomb Lattices
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Neural Geometry for PDEs: Regularity, Stability, and Convergence Guarantees
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Neural likelihood surrogates for parameter inference via log-density PDE
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Neural operators for varying geometry in the forward EMG model.
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Neural-VSI: Variational System Identification of Structural Parameter Fields in High-Order PDEs
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Neuro-Spectral Architectures with Time-Domain Decomposition
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On the Value of Tokeniser Pretraining in Physics Foundation Models
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One Operator to Rule Them All? On Boundary-Indexed Operator Families in Neural PDE Solvers
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OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference
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OtterWeather: Highly Skillful Medium-Range Weather Forecasting on a Single GPU
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Out-of-distribution generalization of deep-learning surrogates for 2D PDE-generated dynamics in the small-data regime
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Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading
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Particle-Guided Diffusion for Gas-Phase Reaction Kinetics
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Physics Informed Neural Networks for Magnetohydrodynamic Equations
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Physics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific
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Physics-Constrained Stochastic ROMs for Unsteady Airfoil Flows
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Physics-Informed Adaptive Training for 3D Acoustic Wave Propagation
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Physics-Informed Conditional Diffusion for Multi-Modal PDEs
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Physics-Informed Deep B-Spline Networks
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Physics-informed fine-tuning of foundation models for partial differential equations
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Physics-Informed Shearlet Neural Operator (PI-ShearletNO) for parametric partial differential equations
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Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators
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POSEIDON: POSEIDON: Physics-Optimized Seismic Energy Inference and Detection Operating Network
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PRESS: Physics-Regularized Parameter Estimation from Steady-State Turing Patterns
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Probabilistic residual transport between multi-fidelity manifolds
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Probabilistic Retrofitting of Learned Simulators
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Re4: Scientific Computing Agent with Rewriting, Resolution, Review and Revision
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Reinforcement Learning Agent for PINN Optimizer Chains
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Relative Position Biases for Transformer PINNs
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Representation Learning for Spatiotemporal Physical Systems
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Resolving Extreme Data Scarcity by Explicit Physics Integration: An Application to Groundwater Heat Transport
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Semi-Lagrangian Physics-Informed Neural Networks (SL-PINNs) for solving hyperbolic Partial Differential Equations (PDEs)
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Smoothness Errors in Dynamics Models and How to Avoid Them
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SoL-DeepONet: Solver-In-The-Loop Deep Operator Networks for Parametric PDEs
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Split Conformal Prediction in the Function Space via Neural Operator Learning
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Supervised Metric Regularization Through Alternating Optimization for Multi-Regime Physics-Informed Neural Networks
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Text-Trained LLMs Can Zero-Shot Extrapolate PDE Dynamics, Revealing a Three-Stage In-Context Learning Mechanism
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The Fractal Neural Operator: Overcoming Spectral Bias in Chaotic Attractors via Prime-Harmonic Weierstrass Encodings
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Time-Splitting Fourier Neural Operator with Coordinate Injection for Scalable Reservoir Simulation
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TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
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TOWARD THE THERMODYNAMIC LIMIT: NEURAL OPERATORS FOR NON-EQUILIBRIUM DYNAMICS OF MOTT INSULATORS
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Towards Efficient and Stable Ocean State Forecasting: A Continuous-Time Koopman Approach
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Towards Uncertainty Quantification in Data-Driven Reduced-Order Models via Bayesian Graph Neural Networks
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UNED: One-shot Uncertainty-aware Neural Experimental Design for Transient PDEs
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Universal Diffusion-Based Probabilistic Downscaling
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What Does a Neural PDE Solver Really Learn? A Residual-Spectrum Diagnostic
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When Does Physics Help? A Systematic Study of Physics-Guided Learning for Robotic Contact Dynamics