Dissipative particle dynamics (DPD) is an off-lattice mesoscopic simulation technique which involves a set of particles moving in continuous space and discrete time. Particles represent whole molecules or fluid regions, rather than single atoms, and atomistic details are not considered relevant to the processes addressed. The particles' internal degrees of freedom are integrated out and replaced by simplified pairwise dissipative and random forces, so as to conserve momentum locally and ensure correct hydrodynamic behaviour. The main advantage of this method is that it gives access to longer time and length scales than are possible using conventional MD simulations. Simulations of polymeric fluids in volumes up to 100 nm in linear dimension for tens of microseconds are now common. DPD was initially devised by Hoogerbrugge and Koelman to avoid the lattice artifacts of the so-called lattice gas automata and to tackle hydrodynamic time and space scales beyond those available with molecular dynamics (MD). It was subsequently reformulated and slightly modified by to ensure the proper thermal equilibrium state. A series of new DPD algorithms with reduced computational complexity and better control of transport properties are presented. The algorithms presented in this article choose randomly a pair particle for applying DPD thermostating thus reducing the computational complexity. (Wikipedia).
Dynamics : An overview of the cause of mechanics
Dynamics is a subset of mechanics, which is the study of motion. Whereas kinetics studies that motion itself, dynamics is concerned about the CAUSES of motion. In particular, it involves the concepts of force, momentum and energy. This video gives an overview of what dynamics is, and is u
From playlist Dynamics
Edriss Titi: Determining the global dynamics of the two-dimensional Navier-Stokes equations by...
One of the main characteristics of infinite-dimensional dissipative evolution equations, such as the Navier-Stokes equations and reaction-diffusion systems, is that their long-time dynamics is determined by finitely many parameters – finite number of determining modes, nodes, volume elemen
From playlist Analysis and its Applications
Determine when a particle is moving down from a position graph
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Particle Motion Problems
What is the displacement of a particle from a position graph
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Particle Motion Problems
How to determine the displacement of a particle from a table
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Particle Motion Problems
Learn how to determine when a particle is at rest using a calculator
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Particle Motion Problems
When is the particle increasing decreasing from a velocity graph
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Particle Motion Problems
Visualization of Quantum Physics (Quantum Mechanics)
This video visually demonstrates some basic quantum physics concepts using the simple case of a free particle. All the simulations here are based on real equations and laws. See more information here: https://www.udiprod.com/quantum-physics/ The mathematics involved was taken from this
From playlist Animated Physics Simulations
Entanglement Dynamics and Dissipation by Vincenzo Alba
DISCUSSION MEETING HYDRODYNAMICS AND FLUCTUATIONS - MICROSCOPIC APPROACHES IN CONDENSED MATTER SYSTEMS (ONLINE) ORGANIZERS: Abhishek Dhar (ICTS-TIFR, India), Keiji Saito (Keio University, Japan) and Tomohiro Sasamoto (Tokyo Institute of Technology, Japan) DATE & TIME: 06 September 2021
From playlist Hydrodynamics and fluctuations - microscopic approaches in condensed matter systems (ONLINE) 2021
Determine when a particle's speed is increasing from a graph
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Determine Increasing or Decreasing Function From a Table
Jérémy Faupin : Scattering theory for Lindblad operators
Abstract: In this talk, I will consider a quantum particle interacting with a target. The target is supposed to be localized and the dynamics of the particle is supposed to be generated by a Lindbladian acting on the space of trace class operators. I will discuss scattering theory for such
From playlist Mathematical Physics
Turbulence: Arrow of time & equilibrium-nonequilibrium behaviour by Mahendra Verma
PROGRAM THERMALIZATION, MANY BODY LOCALIZATION AND HYDRODYNAMICS ORGANIZERS: Dmitry Abanin, Abhishek Dhar, François Huveneers, Takahiro Sagawa, Keiji Saito, Herbert Spohn and Hal Tasaki DATE : 11 November 2019 to 29 November 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore How do is
From playlist Thermalization, Many Body Localization And Hydrodynamics 2019
Lagrangian Irreversibility and Eulerian Dissipation in Fully-Developed... by Samriddhi Sankar Ray
DISCUSSION MEETING: 7TH INDIAN STATISTICAL PHYSICS COMMUNITY MEETING ORGANIZERS : Ranjini Bandyopadhyay, Abhishek Dhar, Kavita Jain, Rahul Pandit, Sanjib Sabhapandit, Samriddhi Sankar Ray and Prerna Sharma DATE: 19 February 2020 to 21 February 2020 VENUE: Ramanujan Lecture Hall, ICTS Ba
From playlist 7th Indian Statistical Physics Community Meeting 2020
Phases, collective modes and nonÂequilibrium dynamics of dissipative Rydberg .. by Subhasis Sinha
DATES Monday 20 Jun, 2016 - Wednesday 29 Jun, 2016 VENUE Ramanujan Lecture Hall, ICTS Bangalore APPLY Understanding strongly interacting quantum many body systems is one of the major frontiers in present day physics. Condensed matter physics provides a wide panoply of systems where strong
From playlist School on Current Frontiers in Condensed Matter Research
Hydrodynamics, variational principles and integrability (Pedagogical Lecture 3) by Alexander Abanov
PROGRAM: INTEGRABLE SYSTEMS IN MATHEMATICS, CONDENSED MATTER AND STATISTICAL PHYSICS ORGANIZERS: Alexander Abanov, Rukmini Dey, Fabian Essler, Manas Kulkarni, Joel Moore, Vishal Vasan and Paul Wiegmann DATE : 16 July 2018 to 10 August 2018 VENUE: Ramanujan Lecture Hall, ICTS Bangalore
From playlist Integrable​ ​systems​ ​in​ ​Mathematics,​ ​Condensed​ ​Matter​ ​and​ ​Statistical​ ​Physics
Thierry Bodineau -- Fluctuating Boltzmann equation and large deviations for a hard sphere gas
A gas dynamics can be modelled by a billiard made of hard spheres, moving according to the laws of classical mechanics. Initially the spheres are randomly distributed according to a probability measure which is then transported by the flow of the deterministic dynamics. Since the seminal w
From playlist Columbia Probability Seminar
Non-Equilibrium Steady States of Quantum Non-Hermitian Lattice Models by Aashish Clerk
PROGRAM NON-HERMITIAN PHYSICS (ONLINE) ORGANIZERS: Manas Kulkarni (ICTS, India) and Bhabani Prasad Mandal (Banaras Hindu University, India) DATE: 22 March 2021 to 26 March 2021 VENUE: Online Non-Hermitian Systems / Open Quantum Systems are not only of fundamental interest in physics a
From playlist Non-Hermitian Physics (ONLINE)
Hamiltonian Structure of 2D Fluid Dynamics with Broken Parity by Sriram Ganeshan
DISCUSSION MEETING : HYDRODYNAMICS AND FLUCTUATIONS - MICROSCOPIC APPROACHES IN CONDENSED MATTER SYSTEMS (ONLINE) ORGANIZERS : Abhishek Dhar (ICTS-TIFR, India), Keiji Saito (Keio University, Japan) and Tomohiro Sasamoto (Tokyo Institute of Technology, Japan) DATE : 06 September 2021 to
From playlist Hydrodynamics and fluctuations - microscopic approaches in condensed matter systems (ONLINE) 2021
Motility-regulation and (self-)organization in Active Matter by Julien Tailleur
PROGRAM : FLUCTUATIONS IN NONEQUILIBRIUM SYSTEMS: THEORY AND APPLICATIONS ORGANIZERS : Urna Basu and Anupam Kundu DATE : 09 March 2020 to 19 March 2020 VENUE : Madhava Lecture Hall, ICTS, Bangalore THIS PROGRAM HAS BEEN MODIFIED ONLY FOR LOCAL (BANGALORE) PARTICIPANTS DUE TO COVID-19 RI
From playlist Fluctuations in Nonequilibrium Systems: Theory and Applications
How to determine when a particle is moving to the left and right
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Particle Motion Problems