Optimal trajectory-guided stochastic co-optimization for e-fuel system design and real-time operation
Department of Chemical Engineering（化学工程系）；Massachusetts Institute of Technology（麻省理工学院）；Department of Chemical and Biomolecular Engineering（化学与生物分子工程系）；Yonsei University（延世大学）；Department of Chemical Engineering and Materials Science（化学工程与材料科学系）；Ewha Womans University（成均馆大学）；Institute for Multiscale Matter and Systems (IMMS)（多尺度物质与系统研究所）
Jeongdong Kim, Minsu Kim, Jonggeol Na, Junghwan Kim
备注：
29 pages, 6 figures. Supplementary Information included
E-fuels are promising long-term energy carriers supporting the net-zero transition. However, the large combinatorial design-operation spaces under renewable uncertainty make the use of mathematical programming impractical for co-optimizing e-fuel production systems. Here, we present MasCOR, a machine-learning-assisted co-optimization framework that learns from global operational trajectories. By encoding system design and renewable trends, a single MasCOR agent generalizes dynamic operation across diverse configurations and scenarios, substantially simplifying design-operation co-optimization under uncertainty. Benchmark comparisons against state-of-the-art reinforcement learning baselines demonstrate near-optimal performance, while computational costs are substantially lower than those of mathematical programming, enabling rapid parallel evaluation of designs within the co-optimization loop. This framework enables rapid screening of feasible design spaces together with corresponding operational policies. When applied to four potential European sites targeting e-methanol production, MasCOR shows that most locations benefit from reducing system load below 50 MW to achieve carbon-neutral methanol production, with production costs of 1.0-1.2 USD per kg. In contrast, Dunkirk (France), with limited renewable availability and high grid prices, favors system loads above 200 MW and expanded storage to exploit dynamic grid exchange and hydrogen sales to the market. These results underscore the value of the MasCOR framework for site-specific guidance from system design to real-time operation.
TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion
Department of Computer and Information Science, University of Pennsylvania（计算机与信息科学系，宾夕法尼亚大学）；School of Mathematics, Georgia Institute of Technology（数学学院，佐治亚理工学院）；Department of Bioengineering, University of Pennsylvania（生物工程系，宾夕法尼亚大学）
RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving
School of Information Technology, Monash University, Malaysia（墨尔本大学信息科技学院）；Faculty of Information Technology, Monash University, Australia（墨尔本大学信息技术学院）；TCS Research, India（印度TCS研究）
Trajectory-Guided Tokenization of Complex CSI for Wi-Fi Sensing
Technische Universität Berlin（柏林工业大学）；Huawei Heisenberg Research Center (Munich)（华为海森堡研究中心（慕尼黑））；Huawei Technologies Co., Ltd., Shanghai, China（华为技术有限公司（上海））；Technical University of Munich（慕尼黑工业大学）
