Research
My research lies at the intersection of continuum mechanics, applied mathematics, and mathematical analysis. I study the mechanics of smart materials and slender structures, with a particular interest in systems where geometry, physics, and mathematics interact in non-trivial ways. The methods I use are predominantly theoretical, drawing on tools from continuum mechanics, asymptotic analysis, homogenization, and dimension reduction.
Each section below links to a dedicated page with a full narrative of the work, including the motivation and the connections between papers.
1. Magnetoelasticity and soft actuators
Magneto-rheological elastomers (MREs) are soft composites whose mechanical response can be tuned by an external magnetic field, making them ideal candidates for contactless actuation. My work in this area includes the development of nonlinear theories for magnetoelastic beams, rods, and shells composed of MREs, where slenderness amplifies deformation through a phenomenon known as huge magnetostriction. A significant ongoing research direction is the optimal design of magnetic cantilevers capable of executing complex motion patterns. Related work addresses shape programming of magnetic elasticae and form-finding strategies for magneto-elastic actuators. → Read more
2. Surface growth and accretion
Surface growth — the process by which material is added or removed at the boundary of a body — underlies phenomena as diverse as actin treadmilling in cell motility, layer-by-layer additive manufacturing, and tumor growth. I have studied the stability of treadmilling regimes, the mechanics of elastic accretion on spherical supports, layered growth models, and the mathematical notion of a four-dimensional reference space needed to capture the kinematics of accretion at finite strain. More recent work addresses surface accretion of pre-stretched half-spaces and focal adhesion detachment. → Read more
3. Thin structures
The derivation of dimensionally reduced theories for plates, shells, and rods is a central theme of my research. I have contributed to the rigorous justification of classical models (Reissner–Mindlin plates, shearable beams) via Gamma-convergence, and to the mechanics of slender elastic ribbons exhibiting helicoidal-to-spiral transitions. More recently, I developed a coordinate-free guide to thin shell theories and studied the mechanics of isostatic thick origami structures. → Read more
4. Diffusion in solids and phase transitions
I have worked on generalized Cahn–Hilliard systems and their applications to solid-state hydrogen storage, including analytical models for stress effects on hydrogen adsorption kinetics in nanoparticles and PDE models capturing hysteresis in absorption. Related work covers thermomechanics of damageable materials under diffusion, doubly nonlinear Cahn–Hilliard systems, and large-strain poroelastic models for geological applications. More recently, I have studied the coupled thermo-hydro-mechanical response of heterogeneous soft materials (hydrogels, edible materials) under intensive heating, analyzing differential dehydration, shape morphing, and the wet-bulb effect in bi-domain systems. → Read more
5. Strain-gradient plasticity
In the context of non-simple materials, I have studied energetic and dissipative scale effects in strain-gradient plasticity, focusing on classical benchmark problems such as torsion of a cylindrical bar and simple shear of a strip. These studies reveal how the length-scale parameters of the theory control the size effect in plastic response, a topic of practical relevance for micro- and nano-scale devices. → Read more
6. Biomechanics
Continuum mechanics provides a natural framework for modeling biological tissues. My contributions include models for epithelial tissue elasticity and the role of surface tension in cancer morphogenesis, an ocular biomechanics study of bulb vibrations, a mechanical model of fiber morphogenesis in the liver, and recent work on oncogenic transformation of tubular ducts. → Read more
7. Machine Learning and Data-Driven Model Discovery
A recent line of research applies machine learning — specifically symbolic regression via genetic programming — to infer latent cost functions governing gradient-flow dynamical systems directly from observed time-series data. The motivating case study is plant gravitropism, where the observable reorientation trajectory encodes a hidden optimality principle that symbolic regression is able to reconstruct without prior assumptions on its functional form. This work is developed within the PRIN 2022 project DISCOVER (Data-drIven diSCOvery of latent Variable-modElled Relations), funded by the Italian Ministry of University and Research (€277k, 2024–2026), for which I serve as Principal Investigator. The project’s broader aim is to build a rigorous mathematical and computational framework for recovering interpretable governing equations from data in biological and physical systems. → Read more
The complete list of my papers (including some unpublished manuscripts) can be found on my Google Scholar account.
For a broader overview of my research interests, objectives, and methods, see my research statement.