发表机构
Yonsei University; Bayer AG; Seoul National University（延世大学; 拜耳股份公司; 首尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SC-DS方法，结合扩散模型与孪生网络实现冰盖模型可扩展无似然校准，精度相当且成本低，可应用于现有方法无法处理的全分辨率冰盖模拟场景。
AI 中文摘要
南极冰盖是未来海平面预测不确定性的主要来源，PSU3D-ICE等物理模拟器是研究其演化的核心工具。对这些模拟器进行观测校准颇具挑战：模拟器输出与观测冰厚场具有高维、空间依赖及半连续特性，其中零值点质量对应无冰区域，这些特征使得传统高斯过程模拟与基于似然的校准方法不适用于全分辨率场景，且计算上不可行。本文提出由扩散模型与孪生网络引导的序贯校准方法（SC-DS），这是一种全神经框架。在模拟方面，我们开发了单个条件扩散模型，通过全局与局部条件表征输入参数对输出的塑造作用，联合生成冰存在与否的二元模式与连续厚度场。由于模拟器会产生难以处理的似然，我们的无似然校准方法将近似贝叶斯计算中人工选择的距离与容差，替换为由迭代重训练孪生网络学习的概率接受规则，同时结合数据-模型差异调整。将该方法应用于西南极冰盖，结果显示SC-DS的精度与最先进的高斯过程校准相当，计算成本仅为其一小部分，且可扩展至现有方法无法处理的全分辨率域。
英文摘要
The Antarctic ice sheet is a major source of uncertainty in future sea-level projections, and physical simulators such as the PSU3D-ICE model are essential for studying its evolution. Calibrating them against observations is challenging: the simulator outputs and observed ice-thickness fields are high-dimensional, spatially dependent, and semi-continuous, with a large point mass at zero denoting ice-free regions. These features make conventional Gaussian-process emulation and likelihood-based calibration ill-suited and computationally infeasible at full resolution. We propose the sequential calibration method guided by a diffusion model and a Siamese network (SC-DS), a fully neural framework. For emulation, we develop a single conditional diffusion model that jointly generates the binary ice presence--absence pattern and the continuous thickness field, using global and local conditioning to represent how the input parameters shape the output. Because the emulator induces an intractable likelihood, our likelihood-free calibration replaces the hand-chosen distance and tolerance of approximate Bayesian computation with a probabilistic acceptance rule learned by an iteratively retrained Siamese network, together with a data--model discrepancy adjustment. Applied to the West Antarctic Ice Sheet, SC-DS matches the accuracy of state-of-the-art Gaussian-process calibration at a fraction of its computational cost and scales to the full-resolution domain, where existing methods become intractable.
