An automatic time step size determination for non-linear problems, solved by implicit schemes, is presented. The time step calculation is based on the estimation of the integration error. This estimation is calculated from the acceleration difference. It is normalised according to the size of the problem and the integration parameters. This time step control algorithm modifies the time step siz...

Abstract Explicit step-truncation tensor methods have recently proven successful in integrating initial value problems for high-dimensional partial differential equations. However, the combination of non-linearity and stiffness may introduce time-step restrictions which could make explicit integration computationally infeasible. To overcome this problem, we develop a new class implicit rank-ada...

according to interactions in multiple grade classes, the present study aims to compare different class compositions in three different groups of fourth-grade multiple grade students in implicit learning and academic achievement. statistical population consists of all fourth grade public elementary school students in piranshahr. 3 groups of students including 8 girls and 8 boys were randomly sel...

In this paper, an alternating implicit block method for solving two dimensional scalar wave equation is presented. The new method consist of two stages for each time step implemented in alternating directions which are very simple in computation. To increase the speed of computation, a group of adjacent points is computed simultaneously. It is shown that the presented method increase the maximu...

Sequential multi-step cloth manipulation is a challenging problem in robotic manipulation, requiring robot to perceive the state and plan sequence of chained actions leading desired state. Most previous works address this goal-conditioned way, goal observation must be given for each specific task configuration, which not practical efficient. Thus, we present novel planning framework named Foldf...

Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed challenges multi-step prediction (Predicting multiple time steps into future) that is accurate, robust and real-time. This paper proposes novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in (RIPPR), overcomes these challenges. R...

This research paper proposes a novel method, Exponential Moving Average Long Short-Term Memory (EMA LSTM), for multi-step vector output prediction of time series data using deep learning. The method combines the LSTM with exponential moving average (EMA) technique to reduce noise in and improve accuracy prediction. compares performance EMA other commonly used learning models, including LSTM, GR...