Deep Learning & NLP for Computational Chemistry, Biology & Nano-materials

Fri, 22 Dec 2017 00:00:00 +0000

Introduction

Data Driven Modelling

Mon, 09 Jan 2017 00:00:00 +0000

Introduction

Data-driven dynamical systems is a burgeoning field-it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical system theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning.

DMD - Dynamic Mode Decomposition

Dynamic Mode Decomposition (source

Tutorials and Ipython notebook

Barathi Ganesh HB

Deep Learning & NLP for Computational Chemistry, Biology & Nano-materials

Introduction

Data Driven Modelling

Introduction

DMD - Dynamic Mode Decomposition