Massively parallel processing computer technology offers the potential to perform complex three dimensional numerical simulations of blast events in a production environment. In an attempt to evaluate this technology in simulating blast phenomena, the U.S. Army Ballistic Research Laboratory (BRL) adapted one of its blast modeling codes to the Thinking Machines CM-2 (and later CM-200) architecture. The code, which was adapted to the Connection Machine (CM), is BRL-01 D, a quasi-one dimensional, finite difference, single material, polytropic gas fluid dynamics code. This code is primarily used for simulating flow in shock tubes and blast simulators and has been reconfigured for the Connection Machine using the CM Fortran programming language. The performance of the code was measured on the CM for a variety of problem sizes and compiler options, and the performance was compared to that of the original Fortran 77 code on a single processor of a Cray X-MP. The optimum performance of the code on one eighth of a maximum configuration CM-200 is 2.5 times greater than that of the original code on a single processor of a Cray X-MP. The code also proved to be perfectly scalable on the CM architecture, allowing BRL to extrapolate a factor of 20 speedup over the Cray X-MP for a maximum configuration of CM-200. nuclear explosion simulation, shock tubes, computer programming.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

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