Our team is composed of experts spanning physics, engineering, and computer science. By integrating these diverse perspectives, we tackle complex problems from multiple angles, yielding deeper insights and driving the development of innovative solutions. We believe that collaboration and intellectual diversity are the essential catalysts for making significant research breakthroughs.

Meet Our Team

Our laboratory is led by Antonio F. Miguel and Luiz A. O. Rocha, both of whom possess extensive experience in the study of complex flow systems. Their research has achieved significant international recognition, both have been consistently included in the 'World’s Top 2% Scientists' list (a study coordinated by Stanford University) ranking in both the Lifetime Scientific Influence and Annual Scientific Influence categories.

Our Mission

Complex networks are omnipresent in both natural and engineered systems, attracting significant global research attention. From the intricate connectivity of biological systems to critical infrastructures like transportation networks and power grids, these systems play a pivotal role in ensuring the functional integrity of our world.

Since 2024, our laboratory has leveraged advanced analytical and computational tools to model these complex systems. We address fundamental challenges across a diverse range of disciplines, including physics, engineering, biological and medical physics, geofluids, and bioinformatics.

We believe that effective engineering requires a deep physical understanding of the systems we model. By centering our research on non-equilibrium thermodynamics, we establish a rigorous physical baseline. We then bridge the gap between physics and engineering to create innovative solutions that are both high-fidelity and physically sound.

Scientific domains: Design, Experimentation and Simulation

Our research integrates theoretical frameworks with applied methodology across the following key areas:

Non-equilibrium Thermodynamics: Investigating systems far from equilibrium to understand energy transformation and entropy production.
Design Theory and Methodology: Developing robust, systematic approaches to complex engineering design.
Data-Driven Hybrid Modelling: Integrating machine learning with governing physical laws to create high-fidelity simulations.
Design Optimization: Employing algorithms to maximize performance under strict design constraints.
Energy Efficiency and Heat Transfer: Advancing thermal management strategies to improve system sustainability.
Renewable Energy Systems: Modeling to enhance the viability and integration of sustainable energy sources.
Experimental Aerodynamics: Validating theoretical models through empirical testing and flow analysis.