Welcome to the Aerofluids, Learning & Discovery Lab (ALD Lab). We are part of Galcit at Caltech and AeroAstro at MIT
Learning physics without units
Dimensionless learning based on information, Nat. Commun. 16, 9171 (2025)
February 2, 2026
Announcements
Mar 2026: Our paper on “Cause-and-effect approach to turbulence forecasting” has been published in the International Journal of Numerical Methods for Heat & Fluid Flow. Check it here!
Mar 2026: Adrian gave a keynote about “Causal inference for scientific discovery in fluid dynamics” at the 3rd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics. You can check the slides here.
Jan 2026: Check our new pre-print on “Numerically consistent non-Boussinesq subgrid-scale stress model with enhanced convergence“.
Jan 2026: Our new pre-print on “Machine-learning wall model of large-eddy simulation for low- and high-speed flows over rough surfaces“ is out.
Jan 2026: Our article published in Communications Physics has been selected as a Monthly Highlight and is now featured on the journal homepage. Check the paper here!
Jan 2026: Check our new pre-print on “Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge”. You can access more details about the challenges here.
Dec 2025: Our paper on “Observational causality by states and interaction type for scientific discovery” has been published in Communications Physics.
Nov 2025: Our lab presented multiple talks at the APS DFD 2025 Meeting in Houston, TX, sharing the latest advancements in our research. You can check here the abstracts.
Nov 2025: Check our new paper on “Disentangling informative and non-informative dynamics between time signals in chaotic systems” published in Chaos, Solitons and Fractals.
Nov 2025: Check our paper “Bow shock instability at hypersonic speed” associated with our poster winner of the 2024 American Physical Society’s Division of Fluid Dynamics (DFD) Milton van Dyke Award.
View all announcements here
Featured Publications
Y. Yuan and A. Lozano-Duran, Dimensionless learning based on information, Nat. Commun. 16, 9171 (2025).
Á. Martínez-Sánchez, G. Arranz, A. Lozano-Duran, Decomposing causality into its synergistic, unique, and redundant components, Nat. Commun. 15, 9296 (2024).
G. Arranz, Y. Ling, S. Costa, K. Goc, and A. Lozano-Duran. “Building-block-flow computational model for large-eddy simulation of external aerodynamic applications”, Communications Engineering 3:127, 2024.
A. Lozano-Durán and H. J. Bae, “Machine-learning building-block-flow wall model for large-eddy simulation“, J. Fluid Mech., 963, A35 , 2023.
A. Lozano-Durán, G. Arranz, ’’Information-theoretic formulation of dynamical systems: causality, modeling, and control”, Physical Review Research, 2022.
Y. Yuan and A. Lozano-Duran, Limits to extreme event forecasting in chaotic systems, Physica D., 467, 134246, 2024.
View all publications here.