fno

Learning in infinite dimension with neural operators.

This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs

Set of PowerShell scripts to maintain D365FFO (Dynamics 365 for Finance and Operations)

Library to collect NSE data in pandas dataframe

An option payoff visualizer that allows you to add and customize strategies and visualize their payoffs. Site built with React, Material UI and D3.

NodeLab is a simple MATLAB-repository for node-generation and adaptive refinement for testing, and implementing various meshfree methods (including physics-informed neural networks, PINNs and DeepOnet) for solving PDEs in arbitrary domains.

Distributed Fourier Neural Operators

A simple real-time Open Interest & Strategy Profit and Loss Visualizer for Indian Benchmark Indices and F&O Stocks inspired by Sensibull. The app is built with React, Material UI, D3 and Node.

Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators

This repository contains the machine learning projects completed for the class "Deep Learning in Scientific Computing" taught at ETH jointly by Siddhartha Mishra and Benjamin Moseley in Spring 2024. The description of the tasks can be found in the PDFs.

RML-FNML: Transformation functions within RML

Using Finvasia Shoonya api for NSE, BSE, NFO trading using php

A comprehensive dashboard for monitoring stocks, including equities and futures and options (F&O) instruments

FnO Trading Bot in Typescript.

Code for master thesis: "Estimating the Permeability of Porous Media with Fourier Neural Operators"

The deployed web app of HistoricalOptions.in

SEmantically DEscribed MIni FUnction LIbrary(SeMiFuLi), using FNO for the description in RDF

These works are under Prof. Akshay Joshi, Mechanical Engineering Dept., IISc Bangalore. On FNOs (Fourier Neural Networks) in multi-dimensions for material property analysis, in different circumstances.

Report of the IncRML specification for incremental mapping support in RML

My solutions for the Artifficial Intelligence for Scientific Computing class at ETH Zurich