somu15

mastodon

A MOOSE app for structural dynamics, seismic analysis, and risk assessment.

moose

Multiphysics Object Oriented Simulation Environment

BIhNNs

The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural Networks (DNNs), Neural ODEs, and Symplectic Neural Networks (SympNets) used with state-of-the-art sampling schemes like Hamiltonian Monte Carlo (HMC) and the No-U-Turn-Sampler (NUTS).

Bayesian_Ground_Motion_Selection

These are a set of codes for simulating the Conditional Spectrum using a Bayesian Analysis. Simulated ground motions can be conveniently combined with real ground motion data through these codes. For more information, please refer to "A Bayesian Treatment of the Conditional Spectrum Approach for Ground Motion Selection". Report by Somayajulu Dhulipala and Madeleine Flint.

Disf_Hazard

This repo consists of the codes used for a paper titled "DISFUNCTIONALITY HAZARD: A RISK-BASED TOOL TO SUPPORT THE RESILIENT DESIGN OF SYSTEMS SUBJECTED TO SINGLE HAZARDS AND MULTIHAZARDS."

Earthquake_Intensity_Measure_Sufficiency

These are the codes for IM selection paper.

GPmat

Matlab implementations of Gaussian processes and other machine learning tools.

hamiltonian-nn

Code for our paper "Hamiltonian Neural Networks"

Kernel_SeismicRisk

MAIN_Multihazard

Scripts for multihazard resilience assessment using semi-Markov processes non-renewal

somu15/hamiltonian-nn

Code for our paper "Hamiltonian Neural Networks"

somu15/adnuts

An R package for NUTS sampling using ADMB

somu15/arviz

Exploratory analysis of Bayesian models with Python

somu15/awesome-neural-ode

A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

somu15/BIhNNs

The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs) used with state-of-the-art sampling schemes like Hamiltonian Monte Carlo (HMC) and the No-U-Turn-Sampler (NUTS).

somu15/blackbear

BlackBear is a MOOSE-based code for simulating degradation processes in concrete and other structural materials.

somu15/Complex_systems_RNN

somu15/constrained-hamiltonian-neural-networks

somu15/deeponet

Learning nonlinear operators via DeepONet

somu15/deepxde

A library for scientific machine learning and physics-informed learning

somu15/diffusion

Denoising Diffusion Probabilistic Models

somu15/falcon

Fracturing And Liquid CONservation

somu15/gnn-powerflow

Graph Neural Network application in predicting AC Power Flow calculation. Developed with Pytorch Geometric framework. My Master Thesis at Eindhoven University of Technology

somu15/GraphNeuralSolver

somu15/IGAPack-PhaseField

Second and fourth-order adaptive phase field modeling of fracture using PHT-splines in the framework of IGA.

somu15/large_media

A repository for storing large images and movies associated with MOOSE documentation.

somu15/lightning

Build and train PyTorch models and connect them to the ML lifecycle using Lightning App templates, without handling DIY infrastructure, cost management, scaling, and other headaches.

somu15/malamute

Advanced manufacturing modeling and simulation

somu15/mastodon

Mastodon is a MOOSE-based application for nonlinear, three-dimensional seismic soil-structure interaction analysis.

somu15/ML

somu15/modulus

Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods

somu15/moose

Multiphysics Object Oriented Simulation Environment

somu15/neml

Modular consitutive modeling library for structural materials

somu15/neuraluq

somu15/NUTS

python version of the No-U-Turn Sampler (NUTS) from Hoffman & Gelman, 2011

somu15/sfepy

Main SfePy repository

somu15/Small_Pf_code

somu15/somu15.github.io

Source code for website

somu15/sympnets

somu15/torchdiffeq

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.