DETS: An Interval-Censored Evidential Sampling Framework for Cross-Domain Scientific Discovery

Scientific measurements inherently contain variance. Standard regression, however, treats noisy data as absolute ground truth, leading models to memorize noise rather than physical laws. This overfitting hampers generalization, particularly when transferring from abundant theoretical proxies to scarce experiments. We introduce DETS, a framework replacing rigid point estimation with Physical Tolerance Modeling. Our Interval-Censored Evidential Engine (ICEE) maximizes probability mass within acceptable error margins, explicitly decoupling aleatoric noise from epistemic uncertainty. Using this filtered uncertainty signal, a Thermodynamic Sampling strategy dynamically selects theoretical data to align source and target domains. Experiments across thermodynamics, drug affinity, and bandgap prediction show DETS outperforms state-of-the-art methods. Crucially, it exhibits superior robustness in high-noise, data-scarce settings.