ICLR 2024PastAI for science
ICLR 2024 Workshop on AI4DifferentialEquations In Science
AI4DiffEqtnsInSci @ ICLR 2024
- Submission deadline
- Feb 11, 2024, 12:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (87)
Fetched from OpenReview (v2) on 2026-06-10.
A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
A Novel ML Model for Numerical Simulations Leveraging Fourier Neural Operators
A PHYSICS-INFORMED NEURAL NETWORK FOR COUPLED CALCIUM DYNAMICS IN A CABLE NEURON
Accelerating Neural Differential Equations for Irregularly-Sampled Dynamical Systems Using Variational Formulation
Adaptive Multilevel Neural Networks for Parametric PDEs with Error Estimation
Application of gauge equivariant convolutional neural networks to learning a fixed point action for SU(3) gauge theory
Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis
Applications of Fourier Neural Operators in the Ifmif-Dones Accelerator
Approximating Family of Steep Traveling Wave Solutions to Fisher's Equation with PINNs
AutoBasisEncoder: Pre-trained Neural Field Basis via Autoencoding for Operator Learning
CHAROT: Robustly controlling chaotic PDEs with partial observations
CLIFFORD NEURAL OPERATORS ON ATMOSPHERIC DATA INFLUENCED PARTIAL DIFFERENTIAL EQUATIONS
Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes Dynamics
Comparing PINNs Across Frameworks: JAX, TensorFlow, and PyTorch
Conformalized Physics-Informed Neural Networks
Consistency Matters: Neural ODE Parameters are Dependent on the Training Numerical Method
Continuous-time neural networks for modeling linear dynamical systems
Data-Driven Higher Order Differential Equations Inspired Graph Neural Networks
Data-driven Multi-Fidelity Modelling for Time-dependent Partial Differential Equations using Convolutional Neural Networks
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
DOF: Accelerating High-order Differential Operators with Forward Propagation
Efficient Fourier Neural Operators by Group Convolution and Channel Shuffling
Efficient GPU-Accelerated Global Optimization for Inverse Problems
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
Equivariant Neural Fields For Symmetry Preserving Continous PDE Forecasting
Estimating Field Parameters from Multiphysics Governing Equations with Scarce Data
Extending Deep Learning Emulation Across Parameter Regimes to Assess Stochastically Driven Spontaneous Transition Events
Extension of Physics-informed Neural Networks to Solving Parameterized PDEs
FastVPINNs: A fast, versatile and robust Variational PINNs framework for forward and inverse problems in science
GA-ReLU: an activation function for Geometric Algebra Networks applied to 2D Navier-Stokes PDEs
Galerkin meets Laplace: Fast uncertainty estimation in neural PDEs
Generative PDE Control
Guided Autoregressive Diffusion Models with Applications to PDE Simulation
Hessian Reparametrization for Coarse-grained Energy Minimization
Heteroscedastic uncertainty quantification in Physics-Informed Neural Networks
Hierarchy-based Clifford Group Equivariant Message Passing Neural Networks
INTEGRAL PINNS FOR HYPERBOLIC CONSERVATION LAWS
Integrating Kernel Methods and Deep Neural Networks for Solving PDEs
Investigating the effects of plant diversity on soil thermal diffusivity using Physics- Informed Neural Networks
Investigation of Latent Time-Scales in Neural ODE Surrogate Models
Investigation of Numerical Diffusion in Aerodynamic Flow Simulations with Physics Informed Neural Networks
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
Joint Parameter and Parameterization Inference with Uncertainty Quantification Through Differentiable Programming
Latent Diffusion Transformer with Local Neural Field as PDE Surrogate Model
LEARN TO ADAPT PARAMETRIC SOLVERS UNDER INCOMPLETE PHYSICS
Learning a vector field from snapshots of unidentified particles rather than particle trajectories
Learning iterative algorithms to solve PDEs.
Learning Stochastic Dynamics from Data
Learning The Delay in Delay Differential Equations
Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids
Mathematical Modeling of Spatio-Temporal Disease Spreading Using PDEs for Machine Learning
Mechanistic Neural Networks for Scientific Machine Learning
Minimizing Structural Vibrations via Guided Diffusion Design Optimization
Mixture of Neural Operators: Incorporating Historical Information for Longer Rollouts
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows
Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation using Compact Implicit Layers
MultiSTOP: Solving Functional Equations with Reinforcement Learning
Neural Context Flows for Learning Generalizable Dynamical Systems
Neural Langevin-type Stochastic Differential Equations for Astronomical time series Classification under Irregular Observations
Neural ODE for Multi-channel Attribution
Neural operators with localized integral and differential kernels
Neural Parameter Regression for Explicit Representations of PDE Solution Operators
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
On Representing Electronic Wave Functions with Sign Equivariant Neural Networks
On training Physics-Informed Neural Networks for Oscillating Problems
Optimal Experimental Design for Bayesian Inverse Problems using Energy-Based Couplings
Optimizing Computationally-Intensive Simulations Using a Biologically-Inspired Acquisition Function and a Fourier Neural Operator Surrogate
PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations
Physics-constrained DeepONet for Surrogate CFD models: a curved backward-facing step case
Physics-Informed Koopman Network for time-series prediction of dynamical systems
Physics-Informed Machine Learning for Fluid Flow Prediction in Porous Media
Physics-informed neural networks for sampling
PINA: a PyTorch Framework for Solving Differential Equations by Deep Learning for Research and Production Environments
PointSAGE: Mesh-independent superresolution approach to fluid flow predictions
RBF-PINN: NON-FOURIER POSITIONAL EMBEDDING IN PHYSICS-INFORMED NEURAL NETWORKS
Scaling Transformers for Skillful and Reliable Medium-range Weather Forecasting
Semiparametric Inference and Equation Discovery with the Bayesian Machine Scientist
Solving Poisson Equations using Neural Walk-on-Spheres
Targeted Reduction of Causal Models
The conjugate kernel for efficient training of physics-informed deep operator networks
Traversing Chemical Space with Latent Potential Flows
TUCKER DECOMPOSITION FOR INTERPRETABLE NEURAL ORDINARY DIFFERENTIAL EQUATIONS
Uncertainty Quantification for Fourier Neural Operators
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent PDEs
Verlet Flows: Exact-Likelihood Integrators for Flow-Based Generative Models
XDDPM: EXPLAINABLE DENOISING DIFFUSION PROB- ABILISTIC MODEL FOR SCIENTIFIC MODELING
Zebra: a continuous generative transformer for solving parametric PDEs