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Tags: Science and math
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Draco is a library for compressing and decompressing 3D geometric meshes and point clouds. It is intended to improve the storage and transmission of 3D graphics.

The Rocq Prover is an interactive theorem prover, or proof assistant. It provides a formal language to write mathematical definitions, executable algorithms and theorems together with an environment for semi-interactive development of machine-checked proofs.

ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.

ACADO Toolkit is a software environment and algorithm collection for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control, state and parameter estimation and robust optimization.

Large collection of number systems providing custom arithmetic for mixed-precision algorithm development and optimization for AI, Machine Learning, Computer Vision, Signal Processing, CAE, EDA, control, optimization, estimation, and approximation.

  • arithmetic
  • integer-arithmetic
  • fixed-point-arithmetic
  • rational-arithmetic

open source software for bidimensional and tridimensional remeshing

  • mesh
  • mesh-adaptation
  • anisotropic-mesh-adaptation
  • isotropic-mesh-adaptation

PyKEP is a scientific library providing basic tools for research in interplanetary trajectory design.

  • interplanetary
  • mission-analysis
  • astrodynamics
  • aerospace

Kaapana is an open source toolkit for state of the art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. The name Kaapana comes from the Hawaiian word kaʻāpana, meaning "distributor" or "part".

Trick Simulation Environment. Trick provides a common set of simulation capabilities and utilities to build simulations automatically.

TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524

  • self-attention
  • compound-protein-interaction
  • transformercpi
  • structure-free-virtual-sreening

Interpretable bilinear attention network with domain adaptation improves drug-target prediction.