In this video, learn how to transform a simple list of events in Excel into a fully dynamic monthly calendar. The tutorial covers: - Setting up dates with the SEQUENCE and WEEKDAY functions - Making ...

Abstract: Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existing GPSSMs ...

In this tutorial, we explore the Advanced Model Context Protocol (MCP) and demonstrate how to use it to address one of the most unique challenges in modern AI systems: enabling real-time interaction ...

Volvo has further refined its renowned dynamic steering system with a new function that can detect a tire blowout and immediately take action to keep the truck safely on the road. This new function ...

Scientists usually use a hypergraph model to predict dynamic behaviors. But the opposite problem is interesting, too. What if researchers can observe the dynamics but don't have access to a reliable ...

In this work, we integrate B-spline functions and physics informed learning to form physics-informed deep B-spline networks that can efficiently learn parameterized PDEs with varying initial and ...

This article is devoted to developing a deep learning method for the numerical solution of the partial differential equations (PDEs). Graph kernel neural networks (GKNN) approach to embedding graphs ...

1 Department of Computer Science, Hong Kong Baptist University, Hong Kong, China 2 College of Computer and Information Engineering, Nanjing Tech University, Nanjing, China Predicting the dynamics of ...

Hamiltonian maps are considered a class of dynamical systems that hold meticulous properties used to model a large number of complex dynamical systems. When time flows in dynamical systems with ...