arXiv research

Analyzes properties of stiffness tensors for elastic wave imaging.

In non-linear incompatible elasticity, the configurations are maps from a non-Euclidean body manifold into the ambient Euclidean space, $\mathbb{R}^k$. We prove the $Γ$-convergence of elastic energies for configurations of a converging sequence, $\mathcal{M}_n\to\mathcal{M}$, of body manifolds. This convergence result …

Study of elastic models in non-Euclidean spaces via Γ-convergence.

Model for material elasticity and plasticity using networks.

Statistical arbitrageurs have inelastic demand, contrary to classical models.

Proposes a new model to describe positive volatility-price correlation in commodity markets.

Study on Transfer Elastic Net error bounds and grouping effect.

Non-Euclidean, or incompatible elasticity is an elastic theory for pre-stressed materials, which is based on a modeling of the elastic body as a Riemannian manifold. In this paper we derive a dimensionally-reduced model of the so-called membrane limit of a thin incompatible body. By generalizing classical dimension red…

This paper introduces an elasticity reconstruction method based on local displacement observations of elastic bodies. Sparse reconstruction theory is applied to formulate the underdetermined inverse problems of elasticity reconstruction including unobserved areas. An online local clustering scheme called a superelement…

Study on elastic curves with variable stiffness, derived from bending energy.

Study of red blood cells using elastic surface theory.

A fast method estimates group-adaptive elastic net penalties using co-data.

We propose a robust elastic net (REN) model for high-dimensional sparse regression and give its performance guarantees (both the statistical error bound and the optimization bound). A simple idea of trimming the inner product is applied to the elastic net model. Specifically, we robustify the covariance matrix by trimm…

Elastic Cash adjusts money supply to stabilize interest rates.

Variable selection plays an important role in the high-dimensional data analysis. However the high-dimensional data often induces the strongly correlated variables problem. In this paper, we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate. By a simulation study, we s…

Deformation estimation of elastic object assuming an internal organ is important for the computer navigation of surgery. The aim of this study is to estimate the deformation of an entire three-dimensional elastic object using displacement information of very few observation points. A learning approach with a neural net…

Characterizes null Lagrangians in Cosserat elasticity.

ARGEN method improves variable selection and regularization in high-dimensional sparse models.

Approximate 3D elastic curves with exact constraints

The paper studies rigidity and continuity in nonlinear elasticity on manifolds and hypersurfaces.

Study preserves planar and graphical properties of curves under elastic flow.

EVCL combines VCL and EWC to prevent forgetting new tasks.

A new method for semi-supervised learning of sparse features using elastic-net.

The paper studies the free elastic flow of closed curves and finds their asymptotic shape converges to a circle.

Motivated by the problem of finding an explicit description of a developable narrow Moebius strip of minimal bending energy, which was first formulated by M. Sadowsky in 1930, we will develop the theory of elastic strips. Recently E.L. Starostin and G.H.M. van der Heijden found a numerical description for an elastic Mo…

The elastic net was introduced as a heuristic algorithm for combinatorial optimisation and has been applied, among other problems, to biological modelling. It has an energy function which trades off a fitness term against a tension term. In the original formulation of the algorithm the tension term was implicitly based…

Study on migrating elastic flows of curves across half-planes.

Study gauge freedoms in elastic wave equations and Dirichlet-to-Neumann map.

Solves curve migration problem with elastic flows.

New insights into stability of special curves on spheres.

Due to the advantage of achieving a better performance under weak regularization, elastic net has attracted wide attention in statistics, machine learning, bioinformatics, and other fields. In particular, a variation of the elastic net, adaptive elastic net (AEN), integrates the adaptive grouping effect. In this paper,…

Symmetric elastic knots are found for certain classes with dihedral symmetry.

We study a class of elastic energy functionals for maps between planar domains (among them the so-called squared distance functional) whose critical points (elastic maps) allow a far more complete theory than one would expect from general elasticity theory. For some of these functionals elastic maps even admit a "Weier…

Study online pricing with contextual elasticity and heteroscedastic valuation.

New discrete curves defined in space forms with geometric properties.

This paper improves neural tangent kernels for better generalization and local elasticity.

New SVM model balances sparsity and robustness in noisy data.

We introduce elastic geodesic grids for easy-to-fabricate, deployable structures.

Distributed machine learning training is one of the most common and important workloads running on data centers today, but it is rarely executed alone. Instead, to reduce costs, computing resources are consolidated and shared by different applications. In this scenario, elasticity and proper load balancing are vital to…

Demand variance can result in a mismatch between planned supply and actual demand. Demand shaping strategies such as pricing can be used to shift elastic demand to reduce the imbalance. In this work, we propose to consider elastic demand in the forecasting phase. We present a method to reallocate the historical elastic…

The elastic flow, which is the $L^2$-gradient flow of the elastic energy, has several applications in geometry and elasticity theory. We present stable discretizations for the elastic flow in two-dimensional Riemannian manifolds that are conformally flat, i.e.\ conformally equivalent to the Euclidean space. Examples in…

Paper develops algorithms for sparse linear regression with generalized elastic net penalty.

We derive a continuum model for incompatible elasticity as a variational limit of a family of discrete nearest-neighbor elastic models. The discrete models are based on discretizations of a smooth Riemannian manifold $(M,\mathfrak{g})$, endowed with a flat, symmetric connection $\nabla$. The metric $\mathfrak{g}$ deter…

Unified survey of elastic flow for curves and networks.

Study on closed $p$-elastic curves in hyperbolic and de Sitter planes.

PIE-PINN estimates elastic properties from noisy, low-res displacement data.

Study shows global invertibility in nonlinear elasticity with vanishing self-repulsion term.

Study models deep learning training dynamics using locally elastic SDEs to reveal feature separability.