:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Holl, Philipp, Thuerey, Nils
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2408.08119
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems
by: Goyal, Girnar, et al.
Published: (2025)

Neural Emulator Superiority: When Machine Learning for PDEs Surpasses its Training Data
by: Koehler, Felix, et al.
Published: (2025)

ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks
by: Liu, Qiang, et al.
Published: (2024)

Component-Based Machine Learning for Indoor Flow and Temperature Fields Prediction Latent Feature Aggregation and Flow Interaction
by: Wang, Shaofan, et al.
Published: (2025)

Flow Matching for Posterior Inference with Simulator Feedback
by: Holzschuh, Benjamin, et al.
Published: (2024)

The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization
by: Egele, Romain, et al.
Published: (2024)

Representation Gap: Explaining the Unreasonable Effectiveness of Neural Networks from a Geometric Perspective
by: Perera, David, et al.
Published: (2026)

On the Unreasonable Effectiveness of Last-layer Retraining
by: Hill, John C., et al.
Published: (2025)

Stabilizing Backpropagation Through Time to Learn Complex Physics
by: Schnell, Patrick, et al.
Published: (2024)

Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics
by: Winchenbach, Rene, et al.
Published: (2024)

APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs
by: Koehler, Felix, et al.
Published: (2024)

The Unreasonable Effectiveness of LLMs for Query Optimization
by: Akioyamen, Peter, et al.
Published: (2024)

INC: An Indirect Neural Corrector for Auto-Regressive Hybrid PDE Solvers
by: Wei, Hao, et al.
Published: (2025)

Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems
by: Du, Yutong, et al.
Published: (2025)

The Unreasonable Effectiveness of Data for Recommender Systems
by: Abdou, Youssef
Published: (2026)

Preconditioned FEM-based Neural Networks for Solving Incompressible Fluid Flows and Related Inverse Problems
by: Griese, Franziska, et al.
Published: (2024)

P3D: Scalable Neural Surrogates for High-Resolution 3D Physics Simulations with Global Context
by: Holzschuh, Benjamin, et al.
Published: (2025)

The Unreasonable Effectiveness of Eccentric Automatic Prompts
by: Battle, Rick, et al.
Published: (2024)

PRDP: Progressively Refined Differentiable Physics
by: Bhatia, Kanishk, et al.
Published: (2025)

The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoning
by: Agarwal, Shivam, et al.
Published: (2025)

The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation
by: Tokarchuk, Evgeniia, et al.
Published: (2023)

Differentiability in Unrolled Training of Neural Physics Simulators on Transient Dynamics
by: List, Bjoern, et al.
Published: (2024)

Physics-embedded Fourier Neural Network for Partial Differential Equations
by: Xu, Qingsong, et al.
Published: (2024)

Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
by: Zhang, Borong, et al.
Published: (2022)

Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models
by: Liu, Qiang, et al.
Published: (2023)

The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learning
by: Donghi, Giovanni, et al.
Published: (2025)

Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
by: Du, Yutong, et al.
Published: (2025)

Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens
by: Valmeekam, Karthik, et al.
Published: (2025)

Bias-Constrained Diffusion Schedules for PDE Emulations: Reconstruction Error Minimization and Efficient Unrolled Training
by: Cleï, Constantin Le, et al.
Published: (2026)

A Deep Neural Network Framework for Solving Forward and Inverse Problems in Delay Differential Equations
by: Wang, Housen, et al.
Published: (2024)

Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
by: Sahin, T., et al.
Published: (2023)

Latent Neural Operator for Solving Forward and Inverse PDE Problems
by: Wang, Tian, et al.
Published: (2024)

Self-diffusion for Solving Inverse Problems
by: Luo, Guanxiong, et al.
Published: (2025)

The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
by: Bayati, Mohsen, et al.
Published: (2020)

The Unreasonable Effectiveness of Easy Training Data for Hard Tasks
by: Hase, Peter, et al.
Published: (2024)

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning
by: Winchenbach, Rene, et al.
Published: (2025)

Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
by: Kohl, Georg, et al.
Published: (2023)

PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations
by: Holzschuh, Benjamin, et al.
Published: (2025)

Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
by: Becktepe, Jannis, et al.
Published: (2026)

CRAFT: Conflict-Resolved Aggregation for Federated Training
by: Wang, Ziqi, et al.
Published: (2026)