Georgia Institute of Technology College of Sciences

A brief overview of some integrable and exactly-solvable Schroedinger equations with trigonometric potentials of Calogero-Moser-Sutherland type is given.All of them are characterized bya discrete symmetry of the Hamiltonian given by the affine Weyl group,a number of polynomial eigenfunctions and eigenvalues which are usually quadratic in the quantum number, each eigenfunction is an element of finite-dimensionallinear space of polynomials characterized by the highest root vector, anda factorization property for eigenfunctions. They admitan algebraic form in the invariants of a discrete symmetry group(in space of orbits) as 2nd order differential operator with polynomial coefficients anda hidden algebraic structure. The hidden algebraic structure for $A-B-C-D$-series is related to the universal enveloping algebra $U_{gl_n}$. For the exceptional $G-F-E$-seriesnew infinite-dimensional finitely-generated algebras of differential operatorswith generalized Gauss decomposition property occur.

Rank reduction as an effective technique for dimension reduction is widely used in statistical modeling and machine learning. Modern statistical applications entail high dimensional data analysis where there may exist a large number of nuisance variables. But the plain rank reduction cannot discern relevant or important variables. The talk discusses joint variable and rank selection for predictive learning. We propose to apply sparsity and reduced rank techniques to attain simultaneous feature selection and feature extraction in a vector regression setup. A class of estimators is introduced based on novel penalties that impose both row and rank restrictions on the coefficient matrix. Selectable principle component analysis is proposed and studied from a self-regression standpoint which gives an extension to the sparse principle component analysis. We show that these estimators adapt to the unknown matrix sparsity and have fast rates of convergence in comparison with LASSO and reduced rank regression. Efficient computational algorithms are developed and applied to real world applications.

Mechanical forces play a key role in the shaping of versatile morphologies of thin structures in natural and synthetic systems. The morphology and deformation of thin ribbons, plates and rods and their instabilities are systematically investigated, through both theoretical modeling and table-top experiments. An elasticity theory combining differential geometry and stationarity principles is developed for the spontaneous bending and twisting of ribbons with tunable geometries in presence of mechanical anisotropy. Closed-form predictions are obtained from this theory with no adjustable parameters, and validated with simple, table-top experiments that are in excellent agreement with the theoretical predictions. For large deformation of ribbons and plates, a more general theory is developed to account for mechanical instability (slap-bracelet type) induced by geometric nonlinearity, due to the competition between inhomogeneous bending and mid-plane stretching energy. This comprehensive, reduced parameter model leads to unique predictions about multistability that are validated with a series of table-top experiments. Furthermore, this study has been extended to interpret a different type of snap-through instability that the Venus flytrap has been actively employing to capture insects for millions of years, and the learnt principle is used to guide the design of bio-mimetic flytrap robot.