NeurIPS 2024PastOther
NeurIPS 2024 Workshop on Data-driven and Differentiable Simulations, Surrogates, and Solvers
D3S3 2024
- Submission deadline
- Aug 31, 2024, 11:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (38)
Fetched from OpenReview (v2) on 2026-06-10.
A neural surrogate solver for radiation transfer
Active Learning for Neural PDE Solvers
ADAGE-Diff: Two-level adaptive agent based modelling for differentiable policy design
Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators
BoostMD: Accelerated Molecular Sampling Leveraging ML Force Field Features
Convergence Guarantees for Neural Network-Based Hamilton–Jacobi Reachability
Convolutional Hierarchical Deep Learning Neural Networks-Tensor Decomposition (C-HiDeNN-TD): a scalable surrogate modeling approach for large-scale physical systems
Cost Estimation in Unit Commitment Problems Using Simulation-Based Inference
Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale
Fine-Tuned MLP-Mixer Foundation Models as data-driven Numerical Surrogates?
From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization
Generative Modeling and Data Augmentation for Power System Production Simulation
Generative Neural Reparameterization for Differentiable PDE-Constrained Optimization
GLEAM-AI: Neural Surrogate for Accelerated Epidemic Analytics and Forecasting
Gradient of Clifford Neural Networks
Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks
Improving Generalization of Differentiable Simulator Policies with Sharpness-Aware Optimization
Learnable Subset Perturbations for Understanding Transcriptional Regulatory Redundancy
Learning cure kinetics of frontal polymerization PDEs using differentiable simulations
Learning Generative Interactive Environments By Trained Agent Exploration
Learning SDE Solutions with Neural Stochastic Flows
Model Exploration through Marginal Likelihood Entropy Maximisation
Modelling variation in the forward EMG model.
Neural Operators as Fast Surrogate Models for the Transmission Loss of Parameterized Sonic Crystals
Optimizing the IFMIF-DONES Particle Accelerator with Differentiable Deep Learning Surrogate Models
ParaFIND: Parameter Field Inference on Non-uniform Domains using Neural Network
Projected Low-Rank Gradient in Diffusion-based Models for Inverse Problems
Projected Neural Differential Equations for Power Grid Modeling with Constraints
SepONet: Efficient Large-Scale Physics-Informed Operator Learning
Spatial Shortcuts in Graph Neural Controlled Differential Equations
Stabilizing Reinforcement Learning in Differentiable Simulation of Deformables
Surrogate-based Physical Error Correction for Spectroscopy Quantification
SWOT-based Simulation of River Discharge with Temporal Graph Neural Networks
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Using Parametric PINNs for Predicting Internal and External Turbulent Flows
VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling
Wave Interpolation Neural Operator: Interpolated Prediction of Electric Fields Across Untrained Wavelengths
When Differentiable Programming Meets Spectral PDE Solver