Chemical Integration of ODEs using Idealized Abstract Solutions
원문 초록
In this work, we propose a general inversion framework to non-uniquely invert a very large class of ordinary differential equations (ODEs) into chemical reaction networks. A thorough treatment of the relevant chemical reaction network theory from the literature is given. Various simulation results are provided to augment the selection procedure for the inverse framework, where a previously known kineticization strategy is shown to be deterministically excellent but undesirable in chemical simulations. The utility of the framework is verified by simulating reaction network forms of meaningful ODE systems, and their time series are analyzed. In particular, we provide simulations of deterministic chaotic attractors whose newly discovered reaction networks are non-equivalent with any existing chemical interpretations within the literature, as well as presenting exemplary figures which may form a roadmap to the successful biochemical implementation of the integration of ODE systems.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Review of the Long Horizon Forecasting Problem in Time Series Analysis
The interplay of robustness and generalization in quantum machine learning
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning
LASSO-ODE: A framework for mechanistic model identifiability and selection in disease transmission modeling
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices
A Better Alternative to Piecewise Linear Time Series Segmentation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.