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A Unified View of Double-Weighting for Marginal Distribution Shift

Segovia, J.I; Mazuelas, S.; Liu, A. (2025-03-01)

Supervised classification traditionally assumes that training and testing samples are drawn from the same underlying distribution. However, practical scenarios are often affected by distribution shifts, such as covariate and...

Collocation-based robust variational physics-informed neural networks (CRVPINNs)

Paszyński, Maciej; Los, M.; Służalec, T.; Maczuga, P.; Vilkha, A.; Uriarte, C. (2025-09-01)

Physics-informed neural networks (PINNs) have been widely used to solve partial differential equations (PDEs) through strong residual minimization formulations. Their extension to weak scenarios via Variational PINNs (VPINNs...

Concave Grain Boundaries Stabilized by Boron Segregation for Efficient and Durable Oxygen Reduction

Geng, X.; Vega-Paredes, M.; Lu, X.; Chakraborty, P.; Li, Y.; Scheu, C.; Wang, Z.; Gault, B. (2024-09-17)

The oxygen reduction reaction (ORR) is a critical process that limits the efficiency of fuel cells and metal-air batteries due to its slow kinetics, even when catalyzed by platinum (Pt). To reduce Pt usage, enhancing both th...

Multi-task Online Learning for Probabilistic Load Forecasting

Zaballa, O.; Álvarez, V.; Mazuelas, S. (2024-11-01)

Load forecasting is essential for the efficient, reliable, and cost-effective management of power systems. Load forecasting performance can be improved by learning the similarities among multiple entities (e.g., regions, bui...