Welcome to the Aerofluids, Learning & Discovery Lab (ALD Lab)
We are part of the Aerospace Department at Caltech and AeroAstro at MIT
Building-block flows: a modular approach to turbulence modeling
Building-block-flow computational model for large-eddy simulation of external aerodynamic applications, Commun. Eng. 3, 127 (2024).
February 2, 2026
Announcements
Apr 2026: Check our new pre-print on “HYMOR: An open-source package for modal, non-modal, and receptivity analysis in high-enthalpy hypersonic vehicles”.
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.
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.