jpcurbelo

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

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"

jpcurbelo/3D-Human-Body-Shape_fork

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

jpcurbelo/b2_fork

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

jpcurbelo/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.

jpcurbelo/CWARHM_fork

jpcurbelo/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"

jpcurbelo/getting-job-data-science

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

jpcurbelo/Groebner.jl_simlab

Groebner bases in pure Julia

jpcurbelo/harmonic-oscillator-pinn_fork

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

jpcurbelo/Human-Body-Measurements-using-Computer-Vision

Anthropometric measurement extraction using single image

jpcurbelo/human-body-reshape-DL-paper

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

jpcurbelo/hydroDL_fork

jpcurbelo/jvm-js-fullstack_jesus

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

jpcurbelo/mc-lstm_fork

Experiments with Mass Conserving LSTMs

jpcurbelo/mooc-machine-learning-weather-climate_FORK

jpcurbelo/MPII-Dataset-in-CSV

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

jpcurbelo/neuralhydrology_fork

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

jpcurbelo/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.

jpcurbelo/nn-non-linear-systems

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

jpcurbelo/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.

jpcurbelo/PINNs_exploring

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

jpcurbelo/pydens_fork

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

jpcurbelo/pygalmesh

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

jpcurbelo/rktk_simlab

Exploring "A Runge-Kutta Toolkit"

jpcurbelo/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)

jpcurbelo/sharcnet-scalene_fork

Modern Approaches to Profiling in Python with Scalene

jpcurbelo/TensorDiffEq_fork

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

jpcurbelo/wandb

🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.