Scientists often model physical processes to understand the natural world and
uncover the causation behind observations. Due to unavoidable simplification,
discrepancies often arise between model predictions and actual observations, in
the form of systematic biases, whose impact varies with model completeness.
Classical model inversion methods such as Bayesian inference or regressive
neural networks tend either to overlook biases or make assumptions about their
nature during data preprocessing, potentially leading to implausible results.
Inspired by recent work in inverse graphics, we replace the decoder stage of a
standard autoencoder with a physical model followed by a bias-correction layer.
This generalisable approach simultaneously inverts the model and corrects its
biases in an end-to-end manner without making strong assumptions about the
nature of the biases. We demonstrate the effectiveness of our approach using
two physical models from disparate domains: a complex radiative transfer model
from remote sensing; and a volcanic deformation model from geodesy. Our method
matches or surpasses results from classical approaches without requiring biases
to be explicitly filtered out, suggesting an effective pathway for
understanding the causation of various physical processes.

受逆向图形学研究的启发，我们采用了将标准自编码器的解码器阶段替换为物理模型加偏差校正层的通用方法，以端到端的方式同时逆向模型并校正其偏差，无需对偏差的性质进行假设，我们使用来自不同领域（遥感中的复杂辐射传输模型和地形测量学中的火山变形模型）的两个物理模型验证了方法的有效性，与传统方法相比，我们的方法能够匹配或超越其结果，而无需显式地滤除偏差，从而为理解各种物理过程的因果关系提供了一个有效的路径。