Stars
A library for differentiable nonlinear optimization
State of Health (SoH) and Remaining Useful Life (RUL) prediction for Li-ion batteries based on Physics-Informed Neural Networks (PINN).
High-Performance Symbolic Regression in Python and Julia
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
2021年最新总结，推荐工程师合适读本，计算机科学，软件技术，创业，思想类，数学类，人物传记书籍
Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks
Reliability engineering toolkit for Python - https://reliability.readthedocs.io/en/latest/
Pytorch code for Arxiv Paper: Learning to learn: Meta-Critic Networks for Sample-Efficient Learning
Magenta: Music and Art Generation with Machine Intelligence
Thermal Neural Networks - Learn dynamic thermal networks from data. Application demo on an electric motor.
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
Differentiable predictive control (DPC) policy optimization examples.
Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoo…
Bayesian learning and inference for state space models
A unified software engineering tool for managing and evaluating power transistors
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
The NASA Prognostic Model Package is a Python framework focused on defining and building models for prognostics (computation of remaining useful life) of engineering systems, and provides a set of …