Prabhu Ramachandran

Bio: Prabhu Ramachandran is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Smoothed-particle hydrodynamics & Python (programming language). The author has an hindex of 9, co-authored 43 publications receiving 1178 citations. Previous affiliations of Prabhu Ramachandran include Indian Institute of Technology Madras.

Mayavi: 3D Visualization of Scientific Data

Abstract: Mayavi is a general purpose, open source 3D scientific visualization package that is tightly integrated with the rich ecosystem of Python scientific packages. Mayavi provides a continuum of tools for developing scientific applications, ranging from interactive and script-based data visualization in Python to full-blown custom end-user applications.

Mayavi: a package for 3D visualization of scientific data

Abstract: Mayavi is an open-source, general-purpose, 3D scientific visualization package. It seeks to provide easy and interactive tools for data visualization that fit with the scientific user's workflow. For this purpose, Mayavi provides several entry points: a full-blown interactive application; a Python library with both a MATLAB-like interface focused on easy scripting and a feature-rich object hierarchy; widgets associated with these objects for assembling in a domain-specific application, and plugins that work with a general purpose application-building framework. In this article, we present an overview of the various features of Mayavi, we then provide insight on the design and engineering decisions made in implementing Mayavi, and finally discuss a few novel applications.

PySPH: a reproducible and high-performance framework for smoothed particle hydrodynamics

Abstract: Smoothed Particle Hydrodynamics (SPH) is a general purpose tech- nique to numerically compute the solutions to partial differential equations such as those used to simulate fluid and solid mechanics. The method is grid-free and uses particles to discretize the various properties of interest (such as density, fluid velocity, pressure etc.). The method is Lagrangian and particles are moved with the local velocity. PySPH is an open source framework for Smoothed Particle Hydrodynamics. It is implemented in a mix of Python and Cython. It is designed to be easy to use on multiple platforms, high-performance and support parallel execution. Users write pure-Python code and HPC code is generated on the fly, compiled, and executed. PySPH supports OpenMP and MPI for distributed computing, in a way that is transparent to the user. PySPH is also designed to make it easy to perform reproducible research. In this paper we discuss the design and implementation of PySPH. Background and Introduction

PySPH: a Python-based framework for smoothed particle hydrodynamics

Abstract: PySPH is an open-source, Python-based, framework for particle methods in general and Smoothed Particle Hydrodynamics (SPH) in particular. PySPH allows a user to define a complete SPH simulation using pure Python. High-performance code is generated from this high-level Python code and executed on either multiple cores, or on GPUs, seamlessly. It also supports distributed execution using MPI. PySPH supports a wide variety of SPH schemes and formulations. These include, incompressible and compressible fluid flow, elastic dynamics, rigid body dynamics, shallow water equations, and other problems. PySPH supports a variety of boundary conditions including mirror, periodic, solid wall, and inlet/outlet boundary conditions. The package is written to facilitate reuse and reproducibility. This paper discusses the overall design of PySPH and demonstrates many of its features. Several example results are shown to demonstrate the range of features that PySPH provides.

SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

Abstract: SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.

SciPy 1.0: fundamental algorithms for scientific computing in Python.

Abstract: SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

SchNet - A deep learning architecture for molecules and materials.

Abstract: Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.

Quantum-chemical insights from deep tensor neural networks.

Abstract: Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems. Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in the training data.