Co4ICF: Co-evolving Physics-Informed Surrogate and RL-based Pulse Optimizer for Inertial Confinement Fusion

• URL: http://arxiv.org/abs/2607.10366v1
• Date: Sat, 11 Jul 2026 15:43:27 GMT
• Title: Co4ICF: Co-evolving Physics-Informed Surrogate and RL-based Pulse Optimizer for Inertial Confinement Fusion
• Abstract summary: Co4ICF is a co-evolving framework that couples a physics-informed surrogate with a PPO-based pulse.<n>In the 1D MULTI environment, Co4ICF achieves 146.1% normalized yield based on current laser design baseline.<n>We release a large-scale MULTI-IFE simulation dataset to support future benchmarking.
• Score: 43.450233708268684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Offline-trained surrogates for Inertial Confinement Fusion (ICF) suffer a well-known failure mode that iterative optimizers drive inputs into out-of-distribution (OOD) regions where predictions become unreliable. Here we present Co4ICF, a co-evolving framework that couples a physics-informed surrogate with a PPO-based pulse optimizer. The surrogate is iteratively fine-tuned on policy-induced trajectories, correcting extrapolation errors as the optimizer shifts the input distribution; the optimizer queries this evolving surrogate as a fast environment. In the 1D MULTI environment, Co4ICF achieves 146.1% normalized yield based on current laser design baseline; as a post-hoc cross-fidelity check, the optimized pulse further attains 246.9% normalized yield when directly evaluated in 2D-MULTI without any 2D training or fine-tuning. Budget-matched ablations support that the gains are not explained solely by additional simulation data and are consistent with the co-evolving mechanism playing a key role. We release a large-scale MULTI-IFE simulation dataset to support future benchmarking.

Related papers

• A FEM-Based Surrogate Modelling and Optimization Framework for Physics-Constrained Electromagnetic Coil Design [2.6205603301997855]
  A Python--MPh--COMSOL workflow couples a two-dimensional axisymmetric finite-element method (FEM) to a Matern 5/2 Gaussian-process (GP) probabilistic surrogate.<n>A selected-design check at a common total current preserves the observed BOBYQA--COBYLA--EI-BO ordering.
  arXiv  Detail & Related papers  (2026-08-19T13:27:15Z)
• Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations [14.561544063927228]
  We propose a comprehensive methodology to adapt a deterministic physics foundation model into a highly efficient and accurate generative surrogate.<n>First, we introduce a two-stage inference scheme that combines a large-stride causal auto-regressive model with a non-causal temporal super-resolution network.<n>Second, we present Mean prediction regularization (MPR), a novel training objective that constrains the generative model to predict the deterministic distribution mean under null noise conditioning.
  arXiv  Detail & Related papers  (2026-08-06T14:41:11Z)
• ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion [28.29207284864795]
  We propose a novel zero-surrogate diffusion framework for offline MOO.<n>Perturb-Guide engine is seamlessly interleaved within the unconditional denoising steps of the reverse generation process.<n>Our approach preserves data privacy while achieving hypervolume improvement.
  arXiv  Detail & Related papers  (2026-06-03T05:27:21Z)
• Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization [17.43522044331905]
  We propose SPADE (Support-Proximity Augmented Diffusion Estimation), a novel framework that reimagines forward surrogate modeling through the lens of conditional generative modeling.<n>We show that SPADE achieves state-of-the-art performance across Design-Bench tasks and an LLM data mixture optimization benchmark.
  arXiv  Detail & Related papers  (2026-05-11T21:09:28Z)
• LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models [48.68246945083386]
  Likelihood-Free Policy Optimization (LFPO) is a native framework that maps the concept of vector field flow matching to the discrete token space.<n>LFPO formulates alignment as geometric velocity rectification, which directly optimize denoising logits via contrastive updates.<n>Experiments demonstrate that LFPO not only outperforms state-of-the-art baselines on code and reasoning benchmarks but also accelerates inference by approximately 20% through reduced diffusion steps.
  arXiv  Detail & Related papers  (2026-03-02T07:42:55Z)
• Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning [40.79705260303773]
  Fine-tuning pre-trained diffusion and flow models to optimize downstream utilities is central to real-world deployment.<n>We present Tail-aware Flow Fine-Tuning (TFFT), a principled and efficient distributional fine-tuning algorithm based on the Conditional Value-at-Risk (CVaR)
  arXiv  Detail & Related papers  (2026-02-18T19:00:59Z)
• Beyond VLM-Based Rewards: Diffusion-Native Latent Reward Modeling [58.59644539594293]
  DiNa-LRM is a diffusion-native latent reward model that formulates preference learning directly on noisy diffusion states.<n>Our method introduces a noise-calibrated Thurstone likelihood with diffusion-noise-dependent uncertainty.<n>Across image alignment benchmarks, DiNa-LRM substantially outperforms existing diffusion-based reward baselines.
  arXiv  Detail & Related papers  (2026-02-11T18:57:29Z)
• Bifidelity Karhunen-Loève Expansion Surrogate with Active Learning for Random Fields [0.4899818550820576]
  We present a bifidelity Karhunen-Loeve expansion (KLE) surrogate model for field-valued quantities of interest (QoIs) under uncertain inputs.<n>We form an active learning strategy that adaptively selects new HF evaluations based on the surrogate's generalization error.<n>New HF samples are then acquired by maximizing an expected improvement criterion, targeting regions of high surrogate error.
  arXiv  Detail & Related papers  (2025-11-05T04:14:44Z)
• DiffusionNFT: Online Diffusion Reinforcement with Forward Process [99.94852379720153]
  Diffusion Negative-aware FineTuning (DiffusionNFT) is a new online RL paradigm that optimize diffusion models directly on the forward process via flow matching.<n>DiffusionNFT is up to $25times$ more efficient than FlowGRPO in head-to-head comparisons, while being CFG-free.
  arXiv  Detail & Related papers  (2025-09-19T16:09:33Z)
• Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout [62.73150122809138]
  Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices.<n>We propose the FedDHAD FL framework, which comes with two novel methods: Dynamic Heterogeneous model aggregation (FedDH) and Adaptive Dropout (FedAD)<n>The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and cost (up to 15.0% smaller)
  arXiv  Detail & Related papers  (2025-07-14T16:19:00Z)
• Divergence Minimization Preference Optimization for Diffusion Model Alignment [66.31417479052774]
  Divergence Minimization Preference Optimization (DMPO) is a principled method for aligning diffusion models by minimizing reverse KL divergence.<n>DMPO can consistently outperform or match existing techniques across different base models and test sets.
  arXiv  Detail & Related papers  (2025-07-10T07:57:30Z)
• Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation [49.49868273653921]
  Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving. We introduce Optimal Gaussian Diffusion (OGD) and Estimated Clean Manifold (ECM) Guidance. Our methodology streamlines the generative process, enabling practical applications with reduced computational overhead.
  arXiv  Detail & Related papers  (2024-08-01T17:59:59Z)
• Improving and generalizing flow-based generative models with minibatch optimal transport [90.01613198337833]
  We introduce the generalized conditional flow matching (CFM) technique for continuous normalizing flows (CNFs) CFM features a stable regression objective like that used to train the flow in diffusion models but enjoys the efficient inference of deterministic flow models. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference.
  arXiv  Detail & Related papers  (2023-02-01T14:47:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.