Sachin Shanbhag | FSU Scientific Computing

Biography

I am a Professor in the Department of Scientific Computing at Florida State University. My research lies at the intersection of soft matter, computational science, and data-driven modeling.

Education: Ph.D., University of Michigan, 2004; B.Tech., IIT Bombay, 1999

Research Interests

I work on soft materials (mostly polymers), numerical algorithms, inverse problems, and data-driven modeling. My research is almost entirely curiosity driven: I am on the lookout for the next interesting puzzle. Nevertheless, most of my work falls into the following silos.

Scientific Machine Learning

Data-driven discovery of constitutive relations for complex fluids, sparse approximation, physics-constrained machine learning, Gaussian processes, and uncertainty quantification.

Inverse Problems & Inference

Bayesian and regularization approaches to ill-posed inverse problems, including recovery of relaxation spectra and molecular composition from rheological data.

Rheology of Soft Materials

Multiscale modeling of polymers solutions, melts, and networks; branched polymers and covalent adaptable networks; linear and nonlinear viscoelasticity.

Algorithms & Software

Spectral methods for large amplitude oscillatory shear (LAOS); Markov chain Monte Carlo; faster-than-the-clock algorithms.

Publications

An updated list of my publications is available on my Google Scholar profile. If you are interested in any of these paper, please do not hesitate to reach out via email.

Courses

I have taught the following classes at Florida State University in the past three years:

ISC 5228 Markov Chain Monte Carlo
ISC 3313 Introduction to Scientific Computing (C++)
ISC 4220 Algorithms for Science Applications 1

Previous classes: molecular dynamics, computational materials science, chemical engineering thermodynamics, kinetics, symbolic and numerical computations

Open-Source Software

I develop scientific software for solving inverse problems in rheology and related fields. All code is open source and available on GitHub.

pyReSpect-freq

Python

Extracts continuous and discrete relaxation spectra from complex modulus $G^*(\omega)$ data using Tikhonov regularization and a fast non-negative least-squares solver.

View on GitHub

pyReSpect-time

Python

Companion tool for extracting continuous and discrete relaxation spectra from stress relaxation modulus $G(t)$ data in the time domain.

View on GitHub

All projects on GitHub →