16-20 / PUEBLA JULY 23-27
This workshop is intended primarily for architects and designers interested in learning parametric and generative design applied to the generation and rationalization of complex geometries for their implementation in different design processes. The course will cover basic concepts and methodology to address many design issues through the development of algorithmic tools via a visual programming language and the development of digital fabrication schemes. Rhinoceros 3D and Grasshopper are going to be used as our modeling tools and V-Ray as our rendering engine. Monday to Friday from 10am to 2pm and from 4pm to 8pm 40hrs.
No previous knowledge of Rhinoceros 3D or programming required, CAD background desirable.
Students: 4,000 MXN Professionals: 5,000 MXN Info: workshop@3dmetrica.com 044 55 28790084 www.3dmetrica.com
www.facebook.com/3dmetrica
TALLER DE VERANO ARQUITECTURA PARAMETRICA DISEÑO GENERATIVO RHINO + GRASSHOPPER + V-RAY
TOUR MÉXICO 2012
MEXICALI 25 AL 29 DE JUNIO / CIUDAD DE MÉXICO 2 AL 6 DE JULIO / MORELIA 9 AL 13 DE JULIO / GUADALAJARA 16 AL 20 DE JULIO / PUEBLA 23 AL 27 DE JULIO
Este taller está dirigido principalmente a arquitectos y diseñadores interesados en el aprendizaje del diseño paramétrico y generativo aplicados a la generación y racionalización de geometrías complejas para su implementación en diferentes procesos de diseño. En el curso se abordarán los conceptos básicos y metodología para hacer frente a diversas problemáticas del diseño mediante el desarrollo de herramientas algorítmicas a través de un lenguaje de programación visual y el desarrollo de esquemas de fabricación digital. Se utilizarán Rhinoceros 3D y Grasshopper como herramientas de modelado y V-Ray como motor de renderizado. Lunes a Viernes de 10am a 2pm y de 4pm a 8pm 40 hrs.
No se requieren conocimientos previos de Rhinoceros 3D ni de programación, conocimientos previos de CAD deseables.
Estudiantes: 4,000 MXN Profesionales: 5,000 MXN Info: workshop@3dmetrica.com 044 55 28790084 www.3dmetrica.com
www.facebook.com/3dmetrica
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is :)
Embryo introduces ideas from Genetic Programming (GP) (not to be confused with genetic algorithms) to Grasshopper. It would be nice to get people thinking about working at higher levels of abstraction in a design computing context. It was the late Paul Coates who introduced me to GP in architectural design ten years ago.
Simply put, the idea is to write a program that writes a program (or in Grasshopper, generates a dataflow-based definition). There are good reasons I believe for looking into this outlined in my thesis (a kind of manual for Embryo is given in Chapter 6).
The three examples included should give an idea of what can be done, although I will upload more in due course as these only scratch the surface. Getting Embryo to work with Galapagos proved to be a bit of a challenge. The example included in the zip only shows a simple combinatorial problem that can be solved with Embryo, but the potential to find new parametric definitions for existing geometry is an exciting one.
Please note that there are some known bugs. For example, attempting to delete the component whilst active will crash Grasshopper, an event that I haven't been able to deal with yet. Also, I would advise not creating more than 1,000 components if you can help it!
One final point - I am not a software engineer and nor do I want to become one. Embryo should be seen as an idea more than a polished piece of software, but I'm curious to see if anyone cares or can use it in a meaningful way.
I will put the source up on git in due course if anyone is interested in how to manipulate components on the canvas and the suchlike, or branching to create their own Embryos.
Copyright 2015 John Harding and released under the GNU General Public License
(typical Embryo setup with random input)
(using gene pools and galapagos)
(component list)
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tema della modellazione parametrica con Grasshopper. Questa plug-in di Rhino consente di progettare, confrontandosi con un contesto evolutivo, attraverso la comprensione e l'utilizzo di parametri e componenti che influenzano la rappresentazione e la rendono dinamica componendo algoritmi. Nel corso verranno introdotte le nozioni base di Grasshopper approfondendo le metodologie della progettazione parametrica e le tecniche di modellazione algoritmica per la generazione di forme complesse.
Le informazioni teoriche saranno fornite in maniera accelerata ma organica e contestuale agli argomenti elencati. Per massimizzare i risultati, le lezioni saranno accompagnate da piccole esercitazioni pratiche.Argomenti trattati:- Introduzione alla progettazione parametrica: teoria, esempi, casi studio- Grasshopper: concetti base, logica algoritmica, interfaccia grafica- Nozioni fondamentali: componenti, connessioni, data flow- Funzioni matematiche e logiche, serie, gestione dei dati- Analisi e definizione di curve e superfici- Definizione di griglie e pattern complessi- Trasformazioni geometriche, paneling- Attrattori, image sampler- Data tree: gestione di dati complessiStrutturaIl corso ha una durata di 16 ore programmate nell'arco di 2 giornate con i seguenti orari: i giorni 10/11 e 11/11 dalle 10,00 alle 19,00 con pausa pranzo di un'ora.
PrerequisitiPer affrontare il corso è richiesta una conoscenza di base del software Rhino attraverso esperienze teoriche e pratiche. I partecipanti dovranno venire muniti di proprio laptop e con software Rhinoceros 5 o Rhinocero 4 perfettamente funzionanti.Alla fine del corso, verrà rilasciato l’attestato di partecipazione ad un corso qualificato certificato dalla McNeel, valido anche per l’ottenimento di crediti formativi universitari.
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e systems?
Architecture can engage technologies to make buildings tectonically transforming and becoming aware of the active surroundings investigating data and responding to environmental change.
The 10-day workshop investigates the design of computational kinetic structural systems, which interact with the behavior inherent in the city, environment and population.
The aim of the workshop is to investigate parametric kinetic strategies that transform according to the ever-changing data system. Like architectural cybernetic machines embedded in a smart city, the projects interact with the population and the environment of Rome proposing another layer of urban strategy. These operations take place both by continually detecting the physical and non-physical data via sensors and by transforming their own forms. The complex dynamic interaction approach leads us to discard the imposition of a fixed form and, instead, create and use a computational kinetic artifice.
Initially students attend lectures on current mainstream and academic research as well as tutorials on parametric modeling software, digital fabrication prototyping and robotic assembly. Then applying skills in a team-based structure, pursue computational design research coupling physical analogue experiments with computer-controlled kinetic prototype programming. The proposals are built assembling digitally fabricated parts and electronic devices, like Arduino boards, sensors and servos to interact with a data exchange and regulating form.
For additional informations go to the AA WEBSITE: http://www.aaschool.ac.uk/STUDY/VISITING/rome
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tura digital en corte Láser, corte CNC, impresión 3d, y modelado paramétrico.
Este tercer taller enseña los fundamentos del modelado paramétrico y algunas bases de manufactura digital.
PERFIL DEL ALUMNO QUE INGRESA:
Diseñador, Arquitecto, Artista con conocimientos de Rhinoceros interesados en comenza a modelar paramétrico con Grasshopper para fabricación digital básica.
PERFIL DEL ALUMNO QUE EGRESA:
El alumno terminará con los conocimientos y criterios para el desarrollo de piezas o proyectos utilizando fabricación digital, mejorando y agilizando los flujos de trabajo, así como los criterios fundamentales del Modelado Paramétrico -Generativo.
Taller de modelado paramétrico con Grasshopper
Interfase
Manejo de Datos
Data Volátil
Data Persistente
Rangos y dominios
Atractores
Listas y Cull
Modelado por Layer Object
Análisis Básicos
Conexión de Curvas
Superficies
Análisis de Superficies
Panelización Básica
Relaciones con Excel
Modelado generativo
Fechas: del 8 de Febrero al 1º de Marzo
Días: Sábado
Horarios: de 10 am a 3 pm
Sesiones: 4 de 5hrs
Duración: 20 horas
Precio: $3,000.00…
como 2ECTS - Horario: Jueves y Viernes de 16:00 a 18:00 - Inicio: final de Febrero 2016 -fecha exacta por especificar- - Inscripciones: Envía tus datos (nombre, apellidos, NIF, mail, teléfono) indicando tu preferencia a iamadrid.arquitectura@upm.es
-Aprendizaje del entorno de programación visual para la generación de prototipos dinámicos de proyectos completos. Plataformas de programación basadas en nodos (node-based) para su gestión. -Diseño de algoritmos interrelacionados. Planificar y explicitar procesos. Traducción de procesos a lenguajes de programación. Sintaxis básicas comunes entre todos los lenguajes de programación. -Explorar tanto derivas como objetivos concretos. Programar herramientas de proyecto como parte del proyecto mismo. Explorar el proceso como esencia del proyecto. -Incorporar al diseño datos externos al mismo. Aprender a programar, automatizar y después matizar decisiones. Generar proyectos adaptativos y reactivos en continua reinformación. -Explorar los límites de lo codificado: producción de codigos como asistentes y no como imposiciones. -Interrelacionar decisiones de equipos. Generar marcos y rutinas para el diseño colaborativo. -Explorar topologías y prototipos, entornos de incertidumbre y posibilidades. Manejo de bases de datos y flujos de herencia y transporte de datos. - Generación dinámica, evolutiva y modificable. Producción de herramientas de codigo abierto.
http://dpa-etsam.com/iam/iam-cursos
https://www.facebook.com/iamadridETSAM?fref=ts
+34 91 336 6537 / 6589…
hreads where Thread I solves object A1 and Thread II solves object A2. As soon as A1 is completed, Thread I can move on to object B1 and as soon as A2 completes, Thread II can move on to object B3 (whichever comes first). When both A1 and A2 are complete, we can spawn a new thread (III) to take care of object B2.
If B2 completes before B3, then Thread III will terminate. If B3 completes before B2, then Thread II terminates. Whichever thread is last will pick up execution of object C3. And so on and so forth.
This sort of threading is actually not guaranteed to help much though, as it is likely that the bottleneck components in the network will still need to be handled by a single thread.
A more efficient solution would be to divvy up the execution per component to multiple threads. If you're trying to compute the Curve Closest Point for 10,000 points and your machine contains 4 cores, then we can assign 2,500 points to the first core, 2,500 points to the second core etc.
This approach will actually work when there's only a few bottleneck components and it also means the order in which components are solved is no longer important.
An even more fine-grained approach to threading would be to make the Curve Closest Point function in the Rhino SDK threaded. There's a lot of looping going on in any given Curve CP computation so the curve could be broken up into loose spans where each span is solved by a different core. Then the partial results get consolidated once all threads finish.
The benefit here is that it would be multi-core for everyone, not just Grasshopper components.
The bad news: Some functions in Rhino are not thread-safe. Meaning that data structures such as NurbsCurves cannot be modified from multiple threads at once as it will compromise their validity. You might well end up with invalid curves and quite possible weird crashes. In very bad cases it might even be that a specific function in our SDK can only be running once, so even if you were to duplicate the curve it would still not work.
Until our SDK is thread-safe there can be no global threading in Grasshopper. I don't know where we're headed with this, but I do know that we've started using some threaded algorithms in the display as of Rhino5, so it seems we're at least getting our feet wet.
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David Rutten
david@mcneel.com
Seattle, WA…
Added by David Rutten at 5:47pm on November 17, 2010
difference consists of.
An Evolutionary Solver/Genetic Algorithm is an implementation of Metaheuristics. Metaheuristics tend to be flexible solvers, applicable to a wide variety of problems, fairly easy to implement, but slow. Other examples of Metaheuristic algorithms would be Random Search, Scatter Search, Simulated Annealing and do on. These algorithms are often modelled on physical or biological processes.
Simulated Annealing for example simulates the physical process of annealing (who'd have thunk it), which is basically the slow cooling of a material which allows it to settle into a crystalline lattice, i.e. a low energy distribution of all the atoms. I'm currently adding an SA solver to Galapagos, and in fact just yesterday managed to get the first successful run: http://www.youtube.com/watch?v=VWtYLv-4oP0
Metaheuristics are especially useful for those cases where little is known about the problem ahead of time. If the problem search-space is mathematically well defined (differentiable, especially), then you can use more targeted algorithms such as the Newton-Raphson method, Pareto-search or Uphill search. You can still use these methods on non-differentiable search-spaces, but it involves sampling the local region to death to get an estimate of the differential. This can be a very costly enterprise, especially in high dimensional search-spaces. In a two-dimensional search-space you'll need 3 to get a lame estimate and 4 to get a halfway decent estimate and 8 to get a good estimate. In three-dimensional search space you already need 26 samples, and the number of samples grows exponentially with higher dimensions.
If you have a specific problem you're trying to solve, Metaheuristics are probably not the best solution, even though they may be easiest to program. Rhino uses something akin to Newton-Raphson for certain problems and that's fast enough to run in real-time.
Divide-and-Conquer algorithms are also quite popular. Sometimes they are called Binary-Search or Tree-Search algorithms as well. Their basic premise is to sample the search-space at a few intervals (but enough to capture the needed detail), then find two neighbours with promising values and sample again in between these two. Then repeat. Each new iteration typically doubles accuracy, which is great because then you only need ~30 ~40 iterations to get an answer as good as possible with double-precision floating point accuracy. However not all problems lend themselves well to this sort of search and in higher dimensions it starts getting slow with disconcerting alacrity.
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David Rutten
david@mcneel.com
Poprad, Slovakia…
Added by David Rutten at 1:54am on August 15, 2011