The Exponentially Faster Stick-Slip Dynamics of the Peeling of an Adhesive Tape

The stick-slip dynamics is considered from the nonlinear differential-algebraic equation (DAE) point of view and the peeling dynamics is shown to be a switching differential index DAE model. In the stick-slip regime with bifurcations, the differential index can be arbitrarily high. The time scale of the peeling velocity, the algebraic variable, in this regime is shown to be exponentially faster compared to the angular velocity of the spool and/or the stretch rate of the tape. A homogenization scheme for the peeling velocity which is characterized by the bifurcations is discussed and is illustrated with numerical examples.Comment: 7 figures, 24 page

Rollover Preventive Force Synthesis at Active Suspensions in a Vehicle Performing a Severe Maneuver with Wheels Lifted off

Among the intelligent safety technologies for road vehicles, active suspensions controlled by embedded computing elements for preventing rollover have received a lot of attention. The existing models for synthesizing and allocating forces in such suspensions are conservatively based on the constraint that no wheels lift off the ground. However, in practice, smart/active suspensions are more necessary in the situation where the wheels have just lifted off the ground. The difficulty in computing control in the last situation is that the problem requires satisfying disjunctive constraints on the dynamics. To the authors',knowledge, no efficient solution method is available for the simulation of dynamics with disjunctive constraints and thus hardware realizable and accurate force allocation in an active suspension tends to be a difficulty. In this work we give an algorithm for and simulate numerical solutions of the force allocation problem as an optimal control problem constrained by dynamics with disjunctive constraints. In particular we study the allocation and synthesis of time-dependent active suspension forces in terms of sensor output data in order to stabilize the roll motion of the road vehicle. An equivalent constraint in the form of a convex combination (hull) is proposed to satisfy the disjunctive constraints. The validated numerical simulations show that it is possible to allocate and synthesize control forces at the active suspensions from sensor output data such that the forces stabilize the roll moment of the vehicle with its wheels just lifted off the ground during arbitrary fish-hook maneuvers

Reinforcing POD-based model reduction techniques in reaction-diffusion complex networks using stochastic filtering and pattern recognition

Complex networks are used to model many real-world systems. However, the dimensionality of these systems can make them challenging to analyze. Dimensionality reduction techniques like POD can be used in such cases. However, these models are susceptible to perturbations in the input data. We propose an algorithmic framework that combines techniques from pattern recognition (PR) and stochastic filtering theory to enhance the output of such models. The results of our study show that our method can improve the accuracy of the surrogate model under perturbed inputs. Deep Neural Networks (DNNs) are susceptible to adversarial attacks. However, recent research has revealed that Neural Ordinary Differential Equations (neural ODEs) exhibit robustness in specific applications. We benchmark our algorithmic framework with the neural ODE-based approach as a reference.Comment: 19 pages, 6 figure

Explicitly Constrained Stochastic Differential Equations on Manifolds

In this manuscript we consider Intrinsic Stochastic Differential Equations on manifolds and constrain it to a level set of a smooth function. Such type of constraints are known as explicit algebraic constraints. The system of differential equation and the algebraic constraints is, in combination, called the Stochastic Differential Algebraic Equations (SDAEs). We consider these equations on manifolds and present methods for computing the solution of SDAEs.Comment: 13 pages, 2 Algorithm

Data driven approach to sparsification of reaction diffusion complex network systems

Graph sparsification is an area of interest in computer science and applied mathematics. Sparsification of a graph, in general, aims to reduce the number of edges in the network while preserving specific properties of the graph, like cuts and subgraph counts. Computing the sparsest cuts of a graph is known to be NP-hard, and sparsification routines exists for generating linear sized sparsifiers in almost quadratic running time $O(n^{2 + \epsilon})$. Consequently, obtaining a sparsifier can be a computationally demanding task and the complexity varies based on the level of sparsity required. In this study, we extend the concept of sparsification to the realm of reaction-diffusion complex systems. We aim to address the challenge of reducing the number of edges in the network while preserving the underlying flow dynamics. To tackle this problem, we adopt a relaxed approach considering only a subset of trajectories. We map the network sparsification problem to a data assimilation problem on a Reduced Order Model (ROM) space with constraints targeted at preserving the eigenmodes of the Laplacian matrix under perturbations. The Laplacian matrix ($L = D - A$) is the difference between the diagonal matrix of degrees ($D$) and the graph's adjacency matrix ($A$). We propose approximations to the eigenvalues and eigenvectors of the Laplacian matrix subject to perturbations for computational feasibility and include a custom function based on these approximations as a constraint on the data assimilation framework. We demonstrate the extension of our framework to achieve sparsity in parameter sets for Neural Ordinary Differential Equations (neural ODEs)