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Turbulent boundary layer calculations break down at the point of flow separation when solved with a prescribed boundary layer edge velocity, ue, in what is called the direct method.
This can be alleviated by solving the system in a fully-simultaneous or quasi-simultaneous manner. Details about both methods are available here (https://www.rug.nl/research/portal/files/14407586/root.pdf), pages 38 onwards. Essentially, the fully-simultaneous method combines the inviscid and viscous equations into a single large system of equations, and solves them with Newton iteration.
I have currently implemented an inviscid panel solver entirely in ExplicitComponents. I intend to implement the boundary layer solver also entirely with ExplicitComponents. I am unsure whether coupling these two groups would then result in an execution procedure like the direct method, or whether it would work like the fully-simultaneous method. I note that in the OpenMDAO paper, it is stated that the components are solved "as a single nonlinear system of equations", and that the reformulation from explicit components to the implicit system is handled automatically by OpenMDAO.
Does this mean that if I couple my two analyses (again, consisting purely of ExplicitComponents) and set the group to solve with the Newton solver, I'll get a fully-simultaneous solution 'for free'? This seems too good to be true, as ultimately the component that integrates the boundary layer equations will have to take some prescribed ue as an input, and then will run into the singularity in the execution of its compute() method.
If doing the above would instead make it execute like the direct method and lead to the singularity, (briefly) what changes would I need to make to avoid it? Would it require defining the boundary layer components implicitly?
despite seeming too good to be true, you can in fact change the structure of your system by changing out the top level solver.
If you used a NonlinearBlockGS solver at the tope, it would solve in the weak form. If you used a NewtonSolver at the top, it would solve as one large monolithic system. This property does indeed derive from the unique structure of how OpenMDAO stores things.
There are some caveats. I would guess that your panel code is implemented as a set of intermediate calculations broken up across several components. If thats the case, then the NewtonSolver will be treating each intermediate variable as it it was its own state variable. In other words, you would have more than just delta and u_e as states, but also all the intermediate calculations too.
This is might be somewhat unstable (though it might work just fine, so try it!). You might need a hybrid between the weak and strong forms, that can be achieved via the solve_subsystems option on the NewtonSolver. This approach, is called the Hierarchical Newton Method in section 5.1.2 of the OpenMDAO paper. It will do a sub-iteration of NLBGS for every top level Newton iteration. This acts as a form of nonlinear preconditioner which can help stabilize the strong form. You can limit ho many sub-iterations are done, and in your case you may want to use just 2 or 3 because of the risk of singularity.
How can someone change the cross validation or holdout procedures in mlr so that before testing with the validation set, that same validation set is changed according to a procedure, namely the reusable holdout procedure?
Procedure:
http://insilico.utulsa.edu/wp-content/uploads/2016/10/Dwork_2015_Science.pdf
Short answer: mlr doesn't support that.
Long answer: My experience with differential privacy for machine learning is that in practice it doesn't work as well as advertised. In particular, to apply thresholdout you need a) copious amounts of data and b) the a priori probability that a given classifier will overfit on the given data -- something you can't easily determine in practice. While the paper you reference comes with example code that shows that thresholdout works in this particular case, but the amount of noise added in the code looks like it was determined on an ad-hoc basis; the relationship to the thresholdout algorithm described in the paper isn't clear.
Before differential privacy can be robustly applied in practice in scenarios like that, mlr won't support it.
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More specifically, how do you model a functional program, or one developed using the Functional Style (without classes) using a diagram, and not textual representation.
Functional programmers generally don't have a lot of use for diagrams. Many functional programmers (but not all) find that writing down types is a good way to encapsulate the design relationships that OO programmers put into UML diagrams.
Because mutable state is rare in functional programs, there are no mutable "objects", so it is not usually useful or necessary to diagram relationships among them. And while one function might call another, this property is usually not important to the overall design of a system but only to the implementation of the function doing the calling.
If I were feeling a strong need to diagram a functional program I might use a concept map in which types or functions play the role of concepts.
UML isn't only class diagrams.
Most of the other diagram types (Use case diagrams, activity diagrams, sequence diagrams...) are perfectly applicable for a purely functional programming style. Even class diagrams could still be useful, if you simply don't use attributes and associations and interpret "class" as "collection of related functions".
Functional programmers have their own version of UML, it is called Category Theory.
(There is a certain truth to this, but it is meant to be read with a touch of humour).
UML is a compendium of different types of modeling. If you are talking about the Object Diagram (Class Diagram), well you are not going to find anything that fits your desired use. But if you are talking about an Interaction Diagram (Activity Diagram) or Requirements Diagram (Use Case Diagram), of course they will help you and are part of the UML base.
To model a functional program, using a diagram, and not textual representation, you can use notation like the one used to program in Viskell or Luna
I realize this is an old thread but I'm not understanding the issue here.
A class is merely an abstraction of a concept that ties the functionality of it's methods together in a more human friendly way. For instance, the class WaveGenerator might include the methods Sine, Sawtooth and SquareWave. All three methods are clearly related to the class Generator. However, all three are also stateless. If designed correctly, they don't need to store state information outside of the method. This makes them stateless objects which - if I understand correctly - makes them immutable functions which are a core concept in the functional paradigm.
From a conceptual perspective I don't see any difference between
let Envelope Sine = ...
and
let Envelope Generator.Sine = ...
other than the fact that the latter might provide greater insight into the purpose of the function.
UML is an object approach because at graphical level you can not define functional modeling. A trick is to directly add constraints and notes at model and not in the diagram levels. I mean that you can write a full functional documentation on each model element directly in the metamodel and only display an object view using the UML editor.
This is maybe stupid but I found this demo in French language exactly on the same subject and using EclipseUML Omondo :
OCL and UML 2.2 (demo in French language 3mn): http://www.download-omondo.com/regle_ocl.swf
This demo explains how to add constraints directly on methods at metamodel level. The interesting point of this demo is that using a single model for the entire project allows to be flexible enough to extend traditional UML and avoid SysML, BPMN, DSL additionals models because all information is built on the top of the UML 2.2 metamodel. I don't know if it will be a success but this initiative is very interesting because reduce modeling complexity and open new frontiers !!
I haven't actually tried modelling a large system in UML and then going for a functional implementation, but I don't see why it shouldn't work.
Assuming the implementation was going to be Haskell, I would start by defining the types and their relationships using a class diagram. Allocate functions to classes by their main argument, but bear in mind that this is just an artefact of UML. If it was easier to create a fictional singleton object just to hold all the functions, that would be fine too. If the application needs state then I would have no problem with modelling that in a state chart or sequence diagram. If I needed a custom monad for application-specific sequencing semantics then that might become a stereotype; the goal would be to describe what the application does in domain terms.
The main point is that UML could be used to model a program for functional implementation. You have to keep in mind a mapping to the implementation (and it wouldn't hurt to document it), and the fit is far from exact. But it could be done, and it might even add value.
I guess you could create a class called noclass and put in functions as methods. Also, you might want to split noclass into multiple categories of functions.
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Could you recommend some good articles/notes/tutorials on finite element method (FEM)? I don't deal with advanced math every day, so a tutorial that introduces me to math needed to understand FEM will be great.
My goal is to write my own simulation of deformable bodies (+plasticity). I must use FEM, but it will be good if I will know BEM (Boundary element method) and FDM (Finite difference method) too.
Really understanding Finite Element Methods requires quite a bit of fairly advanced mathematics; unless you have a few years to devote to the cause, let's leave that aside for now.
That said, the basic ideas underlying FEM are fairly simple if you have some experience with ODE solvers. Can you tell us some more about your background and what you really want to learn so that we can suggest some appropriate resources? Do you want to learn the underlying mathematics, or do you just want to learn some cookbook recipes for applying FEM to a certain class of problem?
Your question is unclear. I don't know what you want to know, because it's impossible to tell what you're ignorant of here.
You don't deal with advanced math every day. What do you know about the finite element method? Here are topics you'll need to know:
Statics and dynamics; how to draw free body diagrams
Solid mechanics - strength of materials, elasticity,
Continuum mechanics for large strain models: Lagrangian and Eulerian formulations
Material models - elasticity and plasticity
Partial differential equations
Method of weighted residuals and integral equations
Linear algebra
Numerical methods
Geometric modeling - CAD for geometry and meshing for FEA models
Commercial or open source packages
You don't say whether you want to use a commercial package (ANSYS, NASTRAN, ABAQUS) or something that you'll write.
As far as references go, there are lots of books available now, but they aren't easy to read or absorb. I'd recommend T.J.R. Hughes' Dover book on the subject. It's cheap and good.
But it's not easy.
I just skimmed through the paper. It looks like a survey article, with nothing new to contribute to the state of the art. It covers a lot more than just small strain plasticity of metals. I see fabric models, large strain problems, etc.
It also mentions boundary element methods and finite difference methods. Do you want to know about those, too? Boundary element methods are completely different from finite elements. The former are based on Green's function formulations; the latter use method of weighted residuals.
The paper doesn't have a great deal of depth to it, but it's very broad. What do you want to know?
I don't think it's possible for someone with so little background to write their own. A better place to start would be FENICS.
I can recommend Introduction to Finite Element Methods by Carlos A. Felippa. It is relativly easy to read.
You can find it here, the Chapters are linked on the main page.
Only the direct stiffness method is covered, think of deformations of trusses without time considerations.
It follows a very nice 'hands-on' approach with examples in Mathematica which are well suited for developer folks.
A really good introduction to FEA for beginners is "Practical Stress Analysis with Finite Elements" by Bryan J Mac Donald. This concentrates on stress analysis but shows you how the method works from both a practical and a theoretical point of view. Unlike a lot of other books it is not specific to any particular software and it is written in plain, easy-to-understand language.
There are many methods for representing structure of a program (like UML class diagrams etc.). I am interested if there is a convention which describes programs in a strict, mathematical way. I am especially interested in the use of mathematical notation for this purpose.
An example: Classes are represented as sets (fields, properties) and functions (operating on the elements of sets). A parent class' fields are a subset of child class'. Functions are described in pseudocode which has to look like this and that...
I know that Z Notation has been used to some extent in the formal verification of software, such as the Tokeneer project.
Z Notation
Z Reference Manual
http://www.amazon.com/Concrete-Mathematics-Foundation-Computer-Science/dp/0201558025
Yes, there is, Floyd-Hoare Logic.
There are a lot of way, but i think most of them are inconvenient for expressing the structure since the structure is often not expressable in default mathematical concepts. The main exception is of course functional programing languages. Think about folds (catamorphisme), groups, algebra's etc.
For imperative programming I know of the existence of Z, which uses (pure and extended) lambda calculus set theory and (first order) predicate logic. However, i dont think it's very convenient. The only upside of using mathematics to express structure is the fact that you can prove stuff about it. But if you want to do that, take a look at JML, Spec# or Eiffel.
Depends on what you're trying to accomplish, but going down this road with specific languages can get you into trouble.
For example, see the circle-ellipse discussion on C++ FAQ Lite.
This book applies the deductive method
to programming by affiliating programs
with the abstract mathematical
theories that enable them work. [...]
I believe that Elements of Programming by Alexander Stepanov and Paul McJones, is pretty close to what you are looking for.
Concepts
A concept is a description of
requirements on one or more types
stated in terms of the existence and
properties of procedures, type
attributes, and type functions defined
on the types.
Z, which has already been mentioned, is pretty much what you describe. There are some variants of it for object-oriented modelling, but I think you can get quite far with "standard Z's" schemas if you wish to model classes.
There's also Alloy, which is newer and inspired by Z. Its notation is perhaps a bit closer to object-orientation. It is also analysable, i.e. you can check the models you create whether they fulfill certain conditions, but it cannot prove that properties hold, just attempt to refute within a finite scope.
The article Dependable Software by Design is a nice introduction to Alloy and its ilk, along with a table of available similar tools.
You are looking for functional programming. There are several functional programming languages, and they are all based on a fundamental mathematical theory called the Lambda calculus. Programs written in a functional programming language such as LISP are a mathematical representation of themselves. ;-)
There is a mathematical language which actually describes a program or rather it's operations. You take the initial state and then transform this state until you reach the desired target state. The transformations yield the program code which must be executed.
See the Wikipedia article about Hoare logic.
The basic idea is that for every function (no matter if you put that into a class or into an old style function), you have a pre- and a post-condition. For example, the precondition can be that you have an array which has >= 0 elements. the post-condition is that every element[i] must by <= element[j] for every i <= j.
The usual description would be "the function sorts the array". But the mathematical terms allow you to transform the input (which must match the precondition) into the output (which must match the postcondition).
It's a bit unwieldy to use, especially for more complex programs but some of the examples are pretty impressive. Often, you get really compact code as the result which looks quite complex but works at first try.
I'd like to suggest Algebra of Programming. It's a calculational approach to programs, using Relational Algebra, and Galois Connections.
If you have further interest on this topic, you can find an amazing paper, here, by Shin-Cheng Mu, and José Nuno Oliveira (slides).
Using Relational Algebra and First-Order Logic, also has a nice synergy with Alloy, Functional Programming, and Design by Contract (easily applied to Object-Oriented Programming).