Results 61 to 70 of about 1,716 (212)
Initial State Privacy of Nonlinear Systems on Riemannian Manifolds
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT In this paper, we investigate initial state privacy protection for discrete‐time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances.
Le Liu, Yu Kawano, Antai Xie, Ming Cao
wiley +1 more source
High Voltage, EarlyView.ABSTRACT Electric power systems are undergoing a fundamental transition from synchronous generator (SG)‐dominated systems to inverter‐based resource (IBR)‐dominated systems. Lyapunov‐ and energy‐function‐based direct methods provide an effective way to estimate the stability region, also known as the region of attraction (ROA), and have become a ...
Yifan Zhang +5 more
wiley +1 more source
SDFs from Unoriented Point Clouds using Neural Variational Heat Distances
Computer Graphics Forum, EarlyView.We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from unoriented point clouds. We first compute a small time step of heat flow (middle) and then use its gradient directions to solve for a neural SDF (right). Abstract We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from ...
Samuel Weidemaier +5 more
wiley +1 more source
Existence and comparison results for variational-hemivariational inequalities
Journal of Inequalities and Applications, 2005 We consider a prototype of quasilinear elliptic variational-hemivariational inequalities involving the indicator function of some closed convex set and a locally Lipschitz functional.
Carl S
doaj
Progressive Convex Hull Simplification
Computer Graphics Forum, EarlyView.Abstract Convex hulls are useful as tight bounding proxies for a variety of tasks including collision detection, ray intersection, and distance computation. Unfortunately, the complexity of polyhedral convex hulls grows linearly with their input. We consider the problem of conservatively simplifying a convex hull to a specified number of half‐spaces ...
Alec Jacobson
wiley +1 more source
Infinitely many solutions for an anisotropic differential inclusion on unbounded domains
Electronic Journal of Qualitative Theory of Differential EquationsThe problem deals with the anisotropic $p(x)$-Laplacian operator where $p_i$ are Lipschitz continuous functions $2\leq p_i(x)
Giovany Figueiredo, Abdolrahman Razani
doaj +1 more source
General existence results for nonconvex third order differential inclusions
Electronic Journal of Qualitative Theory of Differential Equations, 2010 In this paper we prove the existence of solutions to the following third order differential inclusion: $$\left\{ \begin{array}{ll} x^{(3)}(t)\in F(t,x(t),\dot{x}(t),\ddot{x}(t))+G(x(t),\dot{x}(t),\ddot{x}(t)), \mbox{ a.e.
Messaoud Bounkhel, B. Al-Senan
doaj +1 more source
Compactly supported detail field for high quality neural implicit surfaces
Computer Graphics Forum, EarlyView.Abstract Neural implicit surfaces are a powerful tool for encoding a surface as the zero level set of a neural function. Trained using gradient‐descent based optimizers, these methods however suffer from a low‐frequency bias that prevents them to fit fine details of the surface.
Guillaume Coiffier, Justine Basselin
wiley +1 more source
Tangent Blow‐Ups for Processing Non‐Manifold Geometry
Computer Graphics Forum, EarlyView.Abstract Many geometry processing pipelines implicitly assume their input data is a manifold, or is sampled from one, with a unique tangent plane at every point. Geometric data, however, routinely contains sharp features like edges, corners, self‐intersections, branching junctions, and other singularities, rendering standard methods ill‐defined at ...
Alice Petrov +3 more
wiley +1 more source
Strictly Conservative Neural Distance Fields
Computer Graphics Forum, EarlyView.Abstract We propose a first method to generate neural unsigned or signed distance fields (SDFs) that are guaranteed to be conservative with respect to a given 3D shape. This means the true distance is never overestimated and the zero‐level set is a bounding volume for the shape. The method makes use of neural network architectures that ensure Lipschitz
I. Ludwig, M. Campen
wiley +1 more source