nmarzz

ai-fin

ANCOM

R scripts for Analysis of Composition of Microbiomes (ANCOM).

aphynity

augment

clic

Find appointments on Clic Sante

contrastive

EulerMaclaurinFormula

MCRN-DA

This project involves developing reduced physical and data models and algorithms for data assimilation. The goal of the project is to address computational challenges in data assimilation of dimensionality and non-Gaussian behavior for model problems and relevant small to medium scale problems. This includes a models from many different possible areas: atmosphere, ocean, combined atmosphere/ocean, ice sheet, glacier, hurricane, ENSO, polar vortex, ecological models, etc.. The basic idea is to create computational conceptual models, both physical and data models, and combine these with standard data assimilation techniques and new techniques developed to take advantage of the structure of these conceptual models. Among the data assimilation techniques to be considered are projected particle filters. Focus is on application to problems with bimodal or multimodal behavior which ties in with work on tipping points. Another emphasis is on applying these techniques to higher dimensional problems to create lower dimensional computational conceptual models. The project involves employing, developing, and applying projected data assimilation techniques, in particular projected particle filters, using the framework developed in (Maclean, VV 2019), while focusing on the use of different state space and observation space projections for increasingly high dimensional models.

Penguin-Placing

pytorch_vision_template

A template for computer vision experiments in Pytorch

nmarzz/MCRN-DA

This project involves developing reduced physical and data models and algorithms for data assimilation. The goal of the project is to address computational challenges in data assimilation of dimensionality and non-Gaussian behavior for model problems and relevant small to medium scale problems. This includes a models from many different possible areas: atmosphere, ocean, combined atmosphere/ocean, ice sheet, glacier, hurricane, ENSO, polar vortex, ecological models, etc.. The basic idea is to create computational conceptual models, both physical and data models, and combine these with standard data assimilation techniques and new techniques developed to take advantage of the structure of these conceptual models. Among the data assimilation techniques to be considered are projected particle filters. Focus is on application to problems with bimodal or multimodal behavior which ties in with work on tipping points. Another emphasis is on applying these techniques to higher dimensional problems to create lower dimensional computational conceptual models. The project involves employing, developing, and applying projected data assimilation techniques, in particular projected particle filters, using the framework developed in (Maclean, VV 2019), while focusing on the use of different state space and observation space projections for increasingly high dimensional models.

nmarzz/EulerMaclaurinFormula

nmarzz/ai-fin

nmarzz/ANCOM

R scripts for Analysis of Composition of Microbiomes (ANCOM).

nmarzz/aphynity

nmarzz/augment

nmarzz/clic

Find appointments on Clic Sante

nmarzz/contrastive

nmarzz/Penguin-Placing

nmarzz/pytorch_vision_template

A template for computer vision experiments in Pytorch

nmarzz/drp_gen

nmarzz/FinRL-Library

A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance. NeurIPS 2020. Please star. 🔥

nmarzz/FRbias

nmarzz/homework

Homework for Math 578

nmarzz/kernels

nmarzz/models

A collection of pre-trained, state-of-the-art models in the ONNX format

nmarzz/nmarzz.github.io

Forked from academicpages/academicpages.github.io

nmarzz/normalizing-flows

A repo for information on and for the development of theory of normalizing flows in neural odes.

nmarzz/poetrybot

nmarzz/randclass

nmarzz/resceleb

nmarzz/rmt

nmarzz/ros_intro

nmarzz/ruler-compass

nmarzz/scores