Abstract: In this talk, I will introduce a novel framework for physics-informed debiasing of machine learning estimators, which we call Simulation-Calibrated Scientific Machine Learning (SCaSML). This approach leverages the structure of physical models to achieve three key objectives: - Unbiased Predictions: It produces unbiased predictions even when the underlying machine learning predictor is biased. - Overcoming Dimensionality Challenges: It mitigates the curse of dimensionality that often affects high-dimensional estimators. - Inference Time Scaling: Improve the machine learning estimation by allocating inference time computation. The SCaSML paradigm integrates a (potentially) biased machine learning algorithm with a de-biasing procedure that is rigorously designed using numerical analysis and stochastic simulation. We dynamically refines and debiases the SCiML predictions during inference by enforcing the physical laws. Our methodology aligns with recent advances in inference-time computation—similar to those seen in the large language model literature—demonstrating that additional computation can enhance ML estimates. Furthermore, we establish a surprising equivalence between our framework and another research direction that utilizes approximate (linearized) solvers to precondition iterative methods. This connection not only bridges two distinct areas of study but also offers new insights and algorithms into improving estimation accuracy in physics-informed machine learning settings.