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Paul Forman (born 1937) is an historian of science and is the retired curator of the Division of Medicine and Science at the National Museum of American History.Forman's primary research focus has been the history of physics, in which he has helped pioneer the application of cultural history to scientific developments.Trinklein PDF Remotely controlled industrial robotic arm and simulation of automated thermal furnace, Prince Mehandi Ratta PDF Route-optimized Energy Management of Connected and Automated Multi-mode Plug-in Hybrid Electric Vehicle using Reduced-order Powertrain Modeling and Dynamic Programming, Neeraj Rama PDF WEIGHT REDUCTION OF A DIFFERENTIAL CASE AND ITS STATIC STRUCTURAL ANALYSIS, Konark Dhananjay Kumbhalkar PDF AGILE–STAGE GATE MANAGEMENT (ASGM): NPD IMPLEMENTATION PRACTICES FROM GLOBAL FIRMS DEVELOPING COMPLEX, PHYSICAL PRODUCTS, John J.
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For this reason, if no other, it is important to put some mathematical spine into our floppy intuitive notion of self-organization.
Only a few measures of self-organization have been proposed; none can be adopted in good intellectual conscience.
Forman is especially known for two controversial historical theses.
The first (often called "the Forman thesis") regards the influence of German culture on early interpretations of quantum mechanics; Forman argued that the culture of Weimar Germany, through its emphasis on acausality, individuality and visualizability (Anschaulichkeit), contributed to the acceptance and interpretation of quantum mechanics.
And this, of course, turned out to be precisely what was required for the solution of those problems in atomic physics which were then at the focus of the physicist’s interest." In support of Forman, Max Jammer and others cite anti-rationalist movements such as existentialism, pragmatism, and logical empiricism as showing a post-war cultural climate that was receptive to the kinds of argument advanced by Bohr, Heisenberg, Dirac and others.
All self-respecting nonlinear scientists know self-organization when they see it: except when we disagree.
In each case I prove the optimality and uniqueness of the \epsilon-machine's representation of the causal architecture, and give reliable algorithms for pattern discovery.
The \epsilon-machine of processes, namely the amount of information needed to specify the state of the \epsilon-machine.