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This paper studies the inverse problem of recovering load magnitudes from stress-intensity-factor profiles along a crack front, using SIFBench finite-element data. It characterizes when the combined load is identifiable and provides an estimator with calibrated uncertainty for ill-posed regimes.
This paper identifies that standard ridge regularization in potential recovery from flow on directed graphs collapses and reverses the ordering of the estimate due to gauge dependence. It proposes a gauge-invariant Dirichlet energy penalty that yields a parameter-insensitive solution and demonstrates robust dynamic range preservation on real clickstream data, with implications for preventing oversmoothing in graph neural networks.
TRACER is a training-free framework for traffic accident reconstruction that formulates the problem as closed-loop structured inference, iteratively refining event-anchored motion hypotheses under geometric and kinematic constraints, achieving improved fidelity and consistency over data-driven and physics-based baselines.
This paper diagnoses the loss landscape of gradient-based inversion for the Gray-Scott reaction-diffusion system, showing that direct backpropagation fails due to flat plateaus and sharp cliffs, while PINN components like residual loss smooth the landscape. The findings provide design implications for PINN-type methods.
PRISM is a decoder-only autoregressive transformer that solves the inverse problem of multilayer thin-film optical coating design by jointly predicting material selection and thickness, achieving state-of-the-art performance with significantly smaller models.
BlockFormer introduces a transformer architecture for solving inverse problems from block-structured interaction maps, such as centromere identification from Hi-C data, using a custom simulator for synthetic training data.