Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

Last update: Jun 16, 2022

Overview

Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

This is the repository for the paper "Single Red Blood Cell Hydrodynamic Traps Via the Generative Design" submitted to Micromachines journal.

In the paper, the evolutionary approach implemented in GEFEST framework was applied to optimize the structure of geometrically-encoded cell traps.

Experiments folder contains the scripts that run the optimization for the described case. Setup contains the wrapper for the COMSOL simulator and the configuration for the simulation environment. Results folder contains the structure of best obtained configuration.

Owner

Natural Systems Simulation Lab

Natural Systems Simulation Lab in ITMO University

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Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

Related tags

Overview

Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

Owner

Natural Systems Simulation Lab

Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation

Random Forests for Regression with Missing Entries

This code uses generative adversarial networks to generate diverse task allocation plans for Multi-agent teams.

SoGCN: Second-Order Graph Convolutional Networks

[AAAI 2022] Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification

existing and custom freqtrade strategies supporting the new hyperstrategy format.

No Code AI/ML platform

MEDS: Enhancing Memory Error Detection for Large-Scale Applications

Public repo for the ICCV2021-CVAMD paper "Is it Time to Replace CNNs with Transformers for Medical Images?"

BRNet - code for Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss function

Official implementation of GraphMask as presented in our paper Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking.

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

Companion repository to the paper accepted at the 4th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities

The full training script for Enformer (Tensorflow Sonnet) on TPU clusters

Deep Learning for Morphological Profiling

Official implementation of SynthTIGER (Synthetic Text Image GEneratoR) ICDAR 2021

Pairwise learning neural link prediction for ogb link prediction

This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.

Reinforcement learning models in ViZDoom environment

Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021]

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, and Adrien Gaidon.