让机器学习数据自带因果机制和不确定性，提升科学模型可信度。
Instrumented data for causal scientific machine learning
- 数据附带生成它的机制模型与不确定性，支持反事实推演
- 实现可验证的图像到模拟的全流程推理，参数可编辑可追溯
- 适合需要可解释性与因果干预的科研场景，如生物、气候建模
科学机器学习的瓶颈不在于模型规模，而在于数据。观测数据只记录结果，不说明原因；模板合成数据虽有明确生成过程，但仅适用于模拟器预设模板，无法覆盖用户实际问题。我们提出第三种可行方案：仪器化数据，即每个数据点都包含其生成机制模型、对模型的显式不确定性估计，以及可执行的反事实族。验证与验证（V&V）框架下的仪器化图像-模拟管道是具体实现：传感器观测转化为完整可追溯、求解器支撑的模拟，具备可编辑参数及传播的随机性/认知不确定性。该数据基座具有案例特异性、机制监督性，并可通过Pearl的do算子支持因果干预。短期内可应用于计算生物学、气候、材料、流体力学与医学影像的验证、审计与代理训练；长期来看，可能催生可被证伪的科学推理基础模型。
原文摘要 · Abstract (English)
Scientific machine learning is limited less by model size than by the data it is trained on. Observational data records what happened but not why; template synthetic data has a known generating process but only for the simulator's template, not the case a user faces. We argue a third option is now operationally feasible: instrumented data, in which every datum carries the mechanistic model that produced it, an explicit uncertainty over that model, and an executable family of counterfactuals. Verification-and-validation (V&V) instrumented image-to-simulation pipelines are one realisation: a sensor observation becomes a fully specified, solver-backed simulation with explicit, editable parameters and a propagated aleatoric/epistemic uncertainty. The substrate is case-specific, mechanistically supervised, and supports causal interventions through Pearl's do-operator. Near-term consequences for validation, auditing, and surrogate training span computational biology, climate, materials, fluid mechanics, and medical imaging; a longer-term, falsifiable implication concerns foundation models for scientific reasoning.
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