Results 41 to 50 of about 11,954,654 (221)

Continuous-Stage Runge–Kutta Approximation to Differential Problems

Axioms, 2022
In recent years, the efficient numerical solution of Hamiltonian problems has led to the definition of a class of energy-conserving Runge–Kutta methods named Hamiltonian Boundary Value Methods (HBVMs).
Pierluigi Amodio, Luigi Brugnano, Felice Iavernaro   +2 more
doaj   +1 more source

Internal Resonance Mismatch‐Controlled Dissipative Bistability Collapse in Coupled Micromechanical Oscillators

Advanced Science, EarlyView.
This paper reveals internal resonance mismatch‐controlled dissipative bistability collapse in a coupled micromechanical resonator. Tuning δIR changes both internal‐mode amplitude and phase‐dependent feedback. For negative mismatch, Duffing bistability survives.
Jianlin Chen, Xin Zhou, Takashiro Tsukamoto, Nan Wang, Shuji Tanaka   +4 more
wiley   +1 more source

Structure-preserving Runge-Kutta methods for stochastic Hamiltonian equations with additive noise [PDF]

, 2014
There has been considerable recent work on the development of energy conserving one-step methods that are not symplectic. Here we extend these ideas to stochastic Hamiltonian problems with additive noise and show that there are classes of Runge-Kutta ...
Burrage, Pamela, Burrage, Kevin
core   +1 more source

Enhanced High Dimensionality and the Information Processing Capacity in Interfered Spin Wave‐Based Reservoir Computing, Achieved With Eight Detectors

Advanced Electronic Materials, EarlyView.
Physical reservoir computing (PRC) based on spin wave interference has demonstrated high computational performance, yet room for improvement remains. In this study, we fabricated this concept PRC with eight detectors and evaluated the impact of the number of detectors using a chaotic time series prediction task.
Sota Hikasa, Wataru Namiki, Daiki Nishioka, Maki Nishimura, Ryo Iguchi, Kazuya Terabe, Takashi Tsuchiya   +6 more
wiley   +1 more source

Approximating High-Order Adversarial Attacks Using Runge−Kutta Methods

Tsinghua Science and Technology
Adversarial attacks craft adversarial examples (AEs) to fool convolution neural networks. The mainstream gradient-based attacks, based on first-order optimization methods, encounter bottlenecks to generate high transferable AEs attacking unknown models ...
Anjie Peng, Guoqiang Shi, Zhi Lin, Hui Zeng, Xing Yang   +4 more
doaj   +1 more source

Learning regime‐dependent governing equations: A symbolic decision tree approach

AIChE Journal, EarlyView.
Abstract Many chemical engineering systems are governed by mechanisms that switch across operating regimes, making the data‐driven discovery of regime‐dependent governing equations essential for predictive modeling, optimization, and control. We propose symbolic decision trees for the data‐driven discovery of regime‐dependent governing equations.
Ilias Mitrai, Tongjia Liu, Gabriel E. Sanoja   +2 more
wiley   +1 more source

High strong order explicit Runge-Kutta methods for stochastic ordinary differential equations

, 1996
The pioneering work of Runge and Kutta a hundred years ago has ultimately led to suites of sophisticated numerical methods suitable for solving complex systems of deterministic ordinary differential equations.
Burrage, Pamela, Burrage, P. M., Burrage, Kevin, Burrage, K.   +3 more
core   +1 more source

Low rank Runge–Kutta methods, symplecticity and stochastic Hamiltonian problems with additive noise [PDF]

, 2012
In this paper we extend the ideas of Brugnano, Iavernaro and Trigiante in their development of HBVM (s,r) methods to construct symplectic Runge–Kutta methods for all values of s and r with s≥r.
Burrage, Pamela, Burrage, Pamela M., Burrage, Kevin   +2 more
core   +2 more sources

Rosenbrock Type Methods for Solving Non-Linear Second-Order in Time Problems

Mathematics, 2021
In this work, we develop a new class of methods which have been created in order to numerically solve non-linear second-order in time problems in an efficient way.
Maria Jesus Moreta
doaj   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source