Generative World Models Enable Predictive Control of Laser Melt Pool Dynamics
cs.LG
Submitted: 2026-10-05
Updated: 2026-10-05
Code: https://github.com/yyyyorky/meltpool_
Terminology
Sources
- 4D reconstruction of alumina laser melt pools at 25 kHz via operando X-ray multi-projection imaging
- A Fast and Generalizable Fourier Neural Operator-Based Surrogate for Melt-Pool Prediction in Laser Processing
- Recurrent World Models Facilitate Policy Evolution
- Diffusion for World Modeling: Visual Details Matter in Atari
- Diffusion Models Are Real-Time Game Engines
- Generative AI for fast and accurate statistical computation of fluids
- PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
- How Far is Video Generation from World Model: A Physical Law Perspective
- Model-Ensemble Trust-Region Policy Optimization
- Phaedra: Learning High-Fidelity Discrete Tokenization for the Physical Science
- Harnessing Discrete Representations For Continual Reinforcement Learning
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- Real-Time Execution of Action Chunking Flow Policies
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks