3 papers
physics.comp-ph2026
Input-schema identifiability limits in physics-informed surrogates for mechanics-governed flow
Daniel Cieslak, Andrzej Czyzewski
Physics-informed and data-driven surrogates are increasingly used to approximate mechanics-governed flow fields, but the target quantities assigned to such models are not always id…
cs.LG2026
Decision-Aware Evaluation of Physics-Informed Surrogates
Daniel CieÅlak, Andrzej Czyżewski
Physics-informed machine learning is often assessed by curve error, although engineering use depends on downstream decisions: ranking candidates, avoiding infeasible designs and li…
eess.IV2025
Dual-Attention U-Net++ with Class-Specific Ensembles and Bayesian Hyperparameter Optimization for Precise Wound and Scale Marker Segmentation
Daniel CieÅlak, Miriam Reca, Olena Onyshchenko +1
Accurate segmentation of wounds and scale markers in clinical images remainsa significant challenge, crucial for effective wound management and automatedassessment. In this study,…