AI 中文总结
研究聚焦聚变能源商业化下的托卡马克优化问题，提出 TokaGrad 这一可微模拟器，它整合多种可微模型，能自洽模拟全放电场景，与优化器耦合可实现多方面优化，为相关自动化、可微优化提供途径。
AI 中文摘要
随着聚变能源从理论可行性迈向商业化，新反应堆概念设计、托卡马克自主控制和高性能场景优化变得越发重要。传统上此类优化任务依赖代价高昂的试错或强力参数搜索。近期可微编程进展改变了数值模拟范式。可微模拟将整个模拟管道表示为一个连通计算图，能通过模拟器内部灵敏度进行基于梯度的直接控制和优化。本文提出 TokaGrad，一个用于全场景建模的端到端可微托卡马克输运模拟器，它自洽地整合了等离子体平衡、输运、加热、L - H 转变和基座形成的可微模型。这是首个能自洽模拟动态全放电场景的可微托卡马克模拟器。研究表明，与基于梯度的优化器耦合时，TokaGrad 可实现反应堆设计优化、执行器控制和全场景波形优化，为燃烧等离子体场景和反应堆概念的自动化、可微优化提供了途径。
英文摘要
As fusion energy moves from theoretical feasibility toward commercialization, design of new reactor concepts, autonomous tokamak control, and high-performance scenario optimization are becoming increasingly important. Traditionally, such optimization tasks have relied on costly trial-and-error or brute-force parameter searches, based on black-box experiments or simulations. Recently, advances in differentiable programming are changing the paradigm of numerical simulation. Unlike conventional simulations, which are typically executed as locally connected step-by-step procedures, differentiable simulation represents the entire simulation pipeline as a connected computational graph. In such a framework, machine parameters, actuator waveforms, and plasma responses are linked through differentiable operations, allowing Jacobians to propagate across the full simulation. This enables direct gradient-based control and optimization using the internal sensitivities of the simulator, rather than treating the simulator as a black box. Here, we present TokaGrad, an end-to-end differentiable tokamak transport simulator for full-scenario modeling, including ramp-up, L-mode operation, and H-mode access. TokaGrad self-consistently integrates differentiable models for plasma equilibrium, transport, heating, L-H transition, and pedestal formation. To our knowledge, this is the first differentiable tokamak simulator capable of self-consistently modeling dynamic full-discharge scenarios where actuators and plasma evolve together with equilibrium, pedestal, and confinement-regime transitions. We demonstrate that, when coupled to gradient-based optimizers, TokaGrad enables reactor-design optimization, actuator control, and full-scenario waveform optimization. This framework provides a pathway toward automated, differentiable optimization of burning-plasma scenarios and reactor concepts.
