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Chemical Process

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/JesperDramsch/70-Years-of-Machine-Learning-in-Geoscience-in-Review

공식 코드

70 years of machine learning in geoscience in review

https://github.com/airi-institute/sensorscan

공식 코드

SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes

https://github.com/ammar-n-abbas/drl-based-decision-support

공식 코드

Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies

https://github.com/dispink/xpt

공식 코드

MAX: Masked Autoencoder for X-ray Fluorescence in Geological Investigation

https://github.com/haitaozhao/Neural-Component-Analysis

공식 코드

Neural Component Analysis for Fault Detection

https://github.com/joachimschaeffer/lavade

공식 코드

Latent Variable Method Demonstrator -- Software for Understanding Multivariate Data Analytics Algorithms

https://github.com/killingbear999/chemical-reactor-foundation-model

공식 코드

Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation

https://github.com/killingbear999/iclrnn

공식 코드

Input Convex Lipschitz RNN: A Fast and Robust Approach for Engineering Tasks

https://github.com/killingbear999/iclstm

공식 코드

Real-Time Machine-Learning-Based Optimization Using Input Convex Long Short-Term Memory Network

https://github.com/maximilianb2/pc-gym

공식 코드

PC-Gym: Benchmark Environments For Process Control Problems

https://github.com/takeshi-ishida/emergence-simulation-of-biological-cell-like-shapes

공식 코드

Emergent simulation of cell-like shapes satisfying the conditions of life using lattice-type multiset chemical model

https://github.com/zavalab/bayesianopt

공식 코드

On the Implementation of a Bayesian Optimization Framework for Interconnected Systems

https://github.com/cog-imperial/concrete_GBT_instance_for_mixed_integer_convex_optimization_with_GBTs_embedded

Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.