Search Results for "hull"

...The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the dataset to include over 520,000 materials within 1 meV/atom of the convex hull as of August 2024. The repository provides datasets, model definitions, and interactive Colabs for exploring these materials, computing decomposition energies, and visualizing chemical families. Additionally, it includes JAX-based implementations of GNoME and Nequip—the latter being used to train interatomic potentials for dynamic simulations.

...Calculate and optimize the following: Minimum plate thickness (DNV/DNVGL-OS-C101) Minimum section modulus of stiffener/plate (DNVGL-OS-C101) Minimum shear area (DNVGL-OS-C101) Buckling (DNVGL-RP-C201) Fatigue for plate/stiffener connection (DNVGL-RP-C203) Compartments (tank pressures) are created automatically. Pressures on external hull (or any other generic location) is defined by specifying equations. You can optimize single plate/stiffener field or multiple. PYTHON: PIP install ANYstructure Code: https://github.com/audunarn/ANYstructure

...Pandas Technical Analysis (Pandas TA) is an easy-to-use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns. Many commonly used indicators are included, such as: Candle Pattern(cdl_pattern), Simple Moving Average (sma) Moving Average Convergence Divergence (macd), Hull Exponential Moving Average (hma), Bollinger Bands (bbands), On-Balance Volume (obv), Aroon & Aroon Oscillator (aroon), Squeeze (squeeze) and many more.

...Add a spaceship in the 3D View under Add, Mesh, Spaceship. Expand the Spaceship tab that appears in the bottom left of the viewport to adjust procedural generation settings. Build the hull. Extrude the front/rear faces several times, adding random translation/scaling/rotation along the way. Add asymmetry to the hull: Pick random faces and extrude them out in a similar manner, reducing in scale each time. Categorize each face by its orientation and generate details on it such as engines, antenna, weapon turrets, lights etc.

...The functions and classes are organized by topics: * abundance maps: FCLS, NNLS, UCLS * classification: AbundanceClassification, NormXCorr, KMeans SAM, SID, SVC * detection: ACE, CEM, GLRT, MatchedFilter, OSP * distance: chebychev, NormXCorr, SAM, SID * endmembers extraction: ATGP, FIPPI, NFINDR, PPI * material count: HfcVd, HySime * noise: Savitzky Golay, MNF, whiten * sigproc: bilateral * sklearn: HyperEstimatorCrossVal, HyperSVC and others * spectro: convex hull quotient, features extraction (tetracorder style), USGS06 lib interface * util: load_ENVI_file, load_ENVI_spec_lib, corr, cov and others The library do an extensive use of the numpy numeric library and can achieve good speed. The library is mature enough and is very usable even if the development is at a beta step.