16 citations · 16 across the 4 of their papers we have counts for
4 papers
AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery
Marco Ruiz, Miguel Arana-Catania, David R. Ardila +1
Environmental time-series causal discovery requires expert decisions about method choice, conditional-independence tests, lag horizons, sample-size adequacy, multiple-testing contr…
Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
Marco Ruiz, Miguel Arana-Catania, David R. Ardila +1
Time-series causal discovery methods rely on assumptions such as stationarity, regular sampling, and bounded temporal dependence. When these assumptions are violated, structure lea…
Conditional Denoising Model as a Physical Surrogate Model
José Afonso, Pedro Viegas, Rodrigo Ventura +1
Surrogate modeling for complex physical systems typically faces a trade-off between data-fitting accuracy and physical consistency. Physics-consistent approaches typically treat ph…
Physics-consistent machine learning: output projection onto physical manifolds
Matilde Valente, Tiago C. Dias, Vasco Guerra +1
Data-driven machine learning models often require extensive datasets, which can be costly or inaccessible, and their predictions may fail to comply with established physical laws.…