PhD Candidate in Statistical Physics · UNED
Carlos Monago
My research concerns the construction of coarse-grained stochastic models of molecular systems. The central problem is to identify, from microscopic simulations, the effective forces, friction, mobility tensors, hydrodynamic interactions, and internal dissipation that govern the dynamics at the mesoscale.
Stochastic coarse-graining of molecular systems.
Research
Dynamic coarse-graining of molecular systems
I study how detailed microscopic systems can be replaced by lower-dimensional stochastic models that preserve the relevant physics. When microscopic degrees of freedom are eliminated, their influence does not disappear: it reappears as effective forces, noise, friction, mobility, memory, and dissipation. My work focuses on identifying and estimating these irreversible contributions in a physically consistent way — so that coarse-grained models reproduce not only equilibrium structure, but also the correct dynamics.
Self-averaging parameter estimation for coarse-grained stochastic models
An inference framework that treats parameter estimation as a dynamical process, recovering both effective forces and state-dependent transport coefficients directly from microscopic trajectories.
Internal friction in a coarse-grained protein model
A bead model of a globular protein showing that solvent drag alone cannot reproduce microscopic velocity correlations — internal friction from eliminated atomic degrees of freedom is essential.
Writing
Technical notes and essays
Photography
Selected photographs
A curated selection will live here: not an archive dump, but a visual counterpart to the research and writing.