SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
The automated discovery of kinetic rate models – methodological frameworks
7 references·0 citing·29 second hop
The automated discovery of kinetic rate models – methodological frameworks (2024)The automated discovery of kinetic rate m…Distributional reinforcement learning for inventory management in multi-echelon supply chains (2022)Distributional reinforcement …Model-based design of transient flow experiments for the identification of kinetic parameters (2019)Model-based design of transie…Mechanistic models versus machine learning, a fight worth fighting for the biological community? (2018)Mechanistic models versus mac…Modelling the scaling properties of human mobility (2010)Modelling the scaling propert…A Comprehensive Guide to Factorial Two-Level Experimentation (2009)A Comprehensive Guide to Fact…The scaling laws of human travel (2006)The scaling laws of human tra…Designs for Discriminating Between Two Rival Models (1965)Designs for Discriminating Be…DESIGN OF EXPERIMENTS IN NON-LINEAR SITUATIONS (1959)A Joint Design Criterion for the Dual Problem of Model Discrimination and Parameter Estimation (1968)Planning Experiments for Discriminating between Models (1974)Robust experiment design via stochastic approximation (1985)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)A survey of results on mobile phone datasets analysis (2015)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Human mobility: Models and applications (2018)Designing robust optimal dynamic experiments (2002)Numerical methods for optimal control problems in design of robust optimal experiments for nonlinear dynamic processes (2004)Model-based design of experiments for parameter precision: State of the art (2007)Pyomo.DOE : An open‐source package for model‐based design of experiments in Python (2022)Information Distortion in a Supply Chain: The Bullwhip Effect (1997)Optimization of conditional value-at-risk (2000)Proceedings of the 2011 Winter Simulation Conference (WSC) (2011)Benefits, challenges, and limitations of inventory control using machine learning algorithms: literature review (2024)Neuroevolution reinforcement learning for multi-echelon inventory optimization with delivery options and uncertain discount (2024)Risk-averse supply chain management via robust reinforcement learning (2024)THE DESIGN OF OPTIMUM MULTIFACTORIAL EXPERIMENTS (1946)Experimental Designs Balanced for the Estimation of Residual Effects of Treatments (1949)Experiments - Planning, Analysis and Parameter Design Optimization (2001)Application of fractional factorial designs to study drug combinations (2012)Combining two-level and three-level orthogonal arrays for factor screening and response surface exploration (2012)Environmental investment decisions of family firms—An analysis of competitor and government influence (2021)Friendship and mobility (2011)Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions (2020)The lost art of mathematical modelling (2023)Aligning generalization between humans and machines (2025)
this work references cited by second hop

References

  • 2006The scaling laws of human travel2,207 cited
  • 2010Modelling the scaling properties of human mobility1,286 cited
  • 2018Mechanistic models versus machine learning, a fight worth fighting for the biological community?380 cited
  • 1965Designs for Discriminating Between Two Rival Models210 cited
  • 2009A Comprehensive Guide to Factorial Two-Level Experimentation171 cited
  • 2019Model-based design of transient flow experiments for the identification of kinetic parameters60 cited
  • 2022Distributional reinforcement learning for inventory management in multi-echelon supply chains32 cited

Cited by

No citing works inside the corpus for this window.

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work