conftrace_

scientific machine learning

23 papers

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Also known as

SCIML

Co-occurring keywords

partial differential equation (222) neural operator (74) physics-informed neural network (97) neural network (6616) domain decomposition (5) foundation model (735) inverse problem (240) curriculum learning (633) hamiltonian prediction (2) fixed-point solving (2)

Papers

Scientifically-Interpretable Reasoning Network (ScIReN): Discovering Hidden Relationships in the Carbon Cycle and Beyond AAAI 2026
Brain Tumor Growth Inversion via Differentiable Neural Surrogates MIDL 2026
Semi-Implicit Neural Ordinary Differential Equations AAAI 2025
Global Convergence of Adjoint-Optimized Neural PDEs JMLR 2025
Automated, Interpretable, and Scalable Scientific Machine Learning AAAI 2025
MMET: A Multi-Input and Multi-Scale Transformer for Efficient PDEs Solving IJCAI 2025
Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models NIPS 2024
PDENNEval: A Comprehensive Evaluation of Neural Network Methods for Solving PDEs IJCAI 2024
Gradients of Functions of Large Matrices NIPS 2024
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning L4DC 2024
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs NIPS 2024
APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs NIPS 2024
Newton Informed Neural Operator for Solving Nonlinear Partial Differential Equations NIPS 2024
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data ICML 2023
D-CIPHER: Discovery of Closed-form Partial Differential Equations NIPS 2023
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data NIPS 2023
Learning Physical Models that Can Respect Conservation Laws ICML 2023
Learning Neural PDE Solvers with Parameter-Guided Channel Attention ICML 2023
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior NIPS 2023
Learning to Accelerate Partial Differential Equations via Latent Global Evolution NIPS 2022
PDEBench: An Extensive Benchmark for Scientific Machine Learning NIPS 2022
Learning Green's functions associated with time-dependent partial differential equations JMLR 2022
Characterizing possible failure modes in physics-informed neural networks NIPS 2021