Emergence in Biological Networks: Soft Matter Coding
Biological networks endow structures in nature with the capacity of adapting to the environment. Understanding the physics that govern the structural adaptability of these organisms is fundamental to design soft matter structures with the ability to reshape and optimize their behavior. We use phase-field methods and other advanced numerical techniques to reproduce the response of fungal networks and slime molds. We then combine them with experiments and robot swarms with the objective of designing bio-inspired, self-organizing, smart soft matter.
Ice Mechanics: Waves, Nanoparticles, and Freeze-casting
Ice is a polycrystalline material that is greatly influenced by the freezing process parameters, which determine the size, orientation, and spatial configuration of the grains. It is relevant in aircraft icing, glacial ice, and planetary science. Ice also has a grain size distribution that ranges in the ultrasonic wavelength. Wave propagation in ice has recently become relevant to Cryoultrasonic NDE, where a piece is embedded in ice to obtain an ideal sonic shape. We use sophisticated multiscale models to study how nanoparticles modify the formation of ice and its impact in ultrasonic wave propagation and other phenomena.
Pushing the Envelope of Numerical Technology:
Finite Elements, Meshfree Methods, and High Performance Computing
Soft matter and biological tissues exhibit complex solid-fluid behaviors, large deformations, and are governed by high-order partial differential equations. Some of them exhibit growth and metabolism. We address new challenges with innovative solutions that move computational mechanics forward. We are experts in Phase-field methods, Local-Max Entropy and other meshfree approximants, and we produce efficient, robust, parallel codes that run in supercomputing facilities. We are currently focused in developing unified Eulerian frameworks for large-scale computing in fluid-structure interaction problems.