The paper proves non-existence of torqued and anti-torqued vector fields on hyperbolic spaces.
problem Existence of torqued and anti-torqued vector fields on hyperbolic spaces.
method Analyzing the properties of conformal scalar functions and their impact on the existence of vector fields.
result Non-existence of proper torqued and anti-torqued vector fields on hyperbolic spaces.
The paper defines and classifies special curves in Riemannian manifolds.
problem Characterizing curves in Riemannian manifolds.
method Defined and characterized anti-torqued slant helices and torqued curves through differential equations.
result Characterized and classified anti-torqued slant helices and torqued curves.
Study rectifying submanifolds with anti-torqued axis in Riemannian manifolds.
problem Characterize submanifolds with anti-torqued axis in Riemannian manifolds.
method Determine necessary and sufficient conditions for anti-torqued vector fields, characterize submanifolds, and derive rectifying submanifolds as warped products.
result Rectifying submanifolds with anti-torqued axis are warped products with specific warping functions.
A novel clustering method uses torque balance to group objects.
problem Grouping similar objects in various scientific fields.
method Inspired by gravitational interactions, a parameter-free clustering algorithm based on mass and distance.
result The algorithm effectively clusters objects regardless of their shape, size, or density.
Characterizes spacetimes using doubly torqued vectors.
problem Classifying spacetimes based on their geometric properties.
method Characterization through doubly torqued vectors and their properties.
result Doubly twisted and Kundt spacetimes can be characterized.
Method estimates fingertip forces, torques, and curvatures from fingernail images.
problem Estimating fingertip forces and curvatures in various contact scenarios.
method Deformation and color distribution analysis of fingernail images using neural networks.
result High accuracy in predicting fingertip forces, torques, and curvatures.
Characterizes a specific type of spacetime using vector fields.
problem Classifying a specific type of spacetime.
method Using vector fields to characterize 1+n doubly twisted spacetimes.
result Simple classification of 1+n doubly-twisted spacetimes.
Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop con…
Data-efficient learning in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. In this paper, we consider one instance of this challenge, the pixels to torques problem, where an agent must learn a closed-loop control policy from pixel i…
ANN with GA optimizes flexible disc design for lower mass and stress.
problem Design flexible disc elements for lower mass and stress without compromising torque transmission and misalignment.
method Artificial Neural Network (ANN) coupled with Genetic Algorithm (GA) for design exploration.
result Optimized designs meet specified criteria with minimum mass and stress.
In classical surface theory there are but few known examples of surfaces admitting nontrivial isometric deformations and fewer still non-simply-connected ones. We consider the isometric deformability question for an immersion x: M \to R^3 of an oriented non-simply-connected surface with constant mean curvature H. We pr…
We give explicit formulæ for Noether invariants associated to Killing vector fields for the variational problem of minimal and constant mean curvature surfaces in 3-manifolds. In the case of homogeneous spaces, such invariants are the flux (associated to translations) and the torque (associated to rotations). Then we f…
Construct intrinsic Langevin dynamics for rigid inclusions on curved surfaces.
problem Stochastic dynamics of rigid inclusions on curved surfaces.
method Cartan's method of moving frames, Hamiltonian equations, intrinsic Langevin equations, Fokker-Planck equation.
result Extracted overdamped equations for accurate simulations of diffusion processes.
The paper classifies hypersurfaces in Riemannian manifolds with constant inner product and torse-forming axes.
problem Understanding hypersurfaces in Riemannian manifolds with specific geometric properties.
method Analyzing hypersurfaces with constant inner product and torse-forming axes.
result Classification of hypersurfaces with torse-forming axes.
This paper explores the capability of deep neural networks to capture key characteristics of vehicle dynamics, and their ability to perform coupled longitudinal and lateral control of a vehicle. To this extent, two different artificial neural networks are trained to compute vehicle controls corresponding to a reference…
Paper provides closed-form time derivatives for rigid body systems.
problem Need for time derivatives of equations of motion in robotics.
method Lie group formulation for rigid body systems to derive closed-form derivatives up to second-order.
result Closed-form equations provide direct insight into system dynamics.
This work proposes a new method for simultaneous probabilistic identification and control of an observable, fully-actuated mechanical system. Identification is achieved by conditioning stochastic process priors on observations of configurations and noisy estimates of configuration derivatives. In contrast to previous w…
Characterizes Lorentzian manifolds with semi-symmetric metric connections.
problem Characterizing Lorentzian manifolds with specific metric connections.
method Analyzing semi-symmetric metric connections with vanishing curvature and recurrent torsion.
result Establishes conditions for perfect fluid and generalized Robertson-Walker spacetimes.
Study of almost Yamabe solitons on Kaehler submersions.
problem Characterizing almost Yamabe solitons on Kaehler submersions.
method Analyzing conditions for solitons to be shrinking, steady, or expanding.
result Characterizations for almost Yamabe solitons in terms of extrinsic horizontal scalar curvature.
Visualizes movement control optimization landscapes to understand why it's hard and how to make it easier.
problem Understanding and optimizing movement control problems in animation research.
method Novel visualizations of high-dimensional control optimization landscapes.
result Trajectory optimization becomes increasingly ill-conditioned with longer trajectories, while parameterizing control as partial target states can act as an efficient preconditioner.
The paper addresses optimal control on Riemannian manifolds, introducing biased splines for robotic systems.
problem Optimal control on Riemannian manifolds with a mathematically natural cometric not capturing true motion cost.
method Encoding torque-based actuators into a cometric, characterizing optimal solutions via a 4th order differential equation.
result Identified a tensor as the geometric source of biasing solutions away from ordinary splines and geodesics.
Modeling inverse dynamics is crucial for accurate feedforward robot control. The model computes the necessary joint torques, to perform a desired movement. The highly non-linear inverse function of the dynamical system can be approximated using regression techniques. We propose as regression method a tensor decompositi…
LIQSS method improves accuracy and efficiency for power system simulations.
problem Accurately modeling and simulating long-duration mission profiles of Naval power systems.
method Linear Implicit Quantized State System (LIQSS) method for stiff, nonlinear, differential algebraic equations.
result LIQSS1 method yields results within 1% accuracy of continuous methods and increases efficiency logarithmically with quantization size.
NDPs embed dynamical systems into neural networks for efficient sensorimotor learning.
problem Training policies directly in raw action spaces limits scalability for continuous tasks.
method Embed dynamical systems into neural networks to learn robot behaviors via demonstrations.
result NDPs outperform prior methods in both imitation and reinforcement learning setups.
Deep learning speeds up engine calibration for varied driving conditions.
problem Optimizing engine operation during transient driving cycles for better fuel economy and emissions.
method Parallel simulation-driven machine learning using a physics-based engine simulator.
result Deep neural network surrogate model predicts engine performance and emissions accurately and quickly.