jpcurbelo Goto Github PK

3d-human-body-shape_fork

[ICIMCS'2017] Official Code for 3D Human Body Reshaping with Anthropometric Modeling

b2_fork

Bertini 2.0: The redevelopment of Bertini in C++.

camels-spat-to-nh

Scripts to explore the CAMELS_spat dataset and process them into the proper format to be used as input for the NH lstm models.

cwarhm_fork

dplhbvrelease_fork

ealstm_regional_modeling_fork

Accompanying code for our HESS paper "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets"

getting-job-data-science

Collection of advice and resources for getting a job as data scientist

groebner.jl_simlab

Groebner bases in pure Julia

harmonic-oscillator-pinn_fork

Code accompanying my blog post: So, what is a physics-informed neural network?

human-body-measurements-using-computer-vision

Anthropometric measurement extraction using single image

human-body-reshape-dl-paper

Official Code for "A methodology for realistic human shape reconstruction from 2D images"

hydrodl_fork

jvm-js-fullstack_jesus

https://github.com/kotlin-hands-on/jvm-js-fullstack

mc-lstm_fork

Experiments with Mass Conserving LSTMs

mooc-machine-learning-weather-climate_fork

mpii-dataset-in-csv

Python Script to Convert .mat structured dataset of MPII Human Pose Annotations Dataset into .csv for more convenient understanding

neuralhydrology_fork

Python library to train neural networks with a strong focus on hydrological applications.

neurodiffeq_fork

A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.

nn-non-linear-systems

Implementation and testing for the method in https://www.scirp.org/journal/paperinformation.aspx?paperid=67010

pdesbynns_fork

This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means of neural networks using TensorFlow.

pinns-tf2_fork

PINNs-TF2, Physics-informed Neural Networks (PINNs) implemented in TensorFlow V2.

pinns_exploring

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

pydens_fork

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

pygalmesh

:spider_web: A Python interface to CGAL's meshing tools

pysindy_fork

A package for the sparse identification of nonlinear dynamical systems from data

rktk_simlab

Exploring "A Runge-Kutta Toolkit"

rootedtrees.jl_simlab

A collection of functionality around rooted trees to generate order conditions for Runge-Kutta methods in Julia for differential equations and scientific machine learning (SciML)

sharcnet-scalene_fork

Modern Approaches to Profiling in Python with Scalene

tensordiffeq_fork

Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

torchhydronodes

Python library to train hybrid hydrologic models (neural networks + conceptual models)