ers and researchers, programmers and artists, professionals and academics who come together for 4 days of intense collaboration, development, and design.
The sg2012 Workshop will be organised around Clusters. Clusters are hubs of expertise. They comprise of people, knowledge, tools, materials and machines. The Clusters provide a focus for workshop participants working together within a common framework.
Clusters provide a forum for the exchange of ideas, processes and techniques and act as a catalyst for design resolution. The Workshop is made up of ten Clusters that respond in diverse ways to the sg2012 Challenge Material Intensities.
Applicants to the sg2012 Workshop will select their preferred cluster from the following:
Beyond Mechanics
Micro Synergetics
Composite Territories
Ceramics 2.0
Material Conflicts
Transgranular Perspiration
Reactive Acoustic Environments
Form Follows Flow
Bioresponsive Building Envelopes
Gridshell Digital Tectonics
More information about the Workshop and Clusters can be found here:
http://smartgeometry.org/index.php?option=com_content&view=article&id=116&Itemid=131
The application process will close on January 15th, 2012.
Full Fee $1500
Reduced Fee $750
Scholarship Fee $350
Fees include attendance to both the workshop and conference from March 19th-24th.
Reduced Fee and Scholarships are available only for Academics, Students and Young Practitioners, and are awarded during a competitive peer review process.
sg2012 takes place from 19-24 March 2012 at EMPAC (http://empac.rpi.edu/) and is hosted by Rensselaer Polytechnic Institute in Troy, upstate New York USA. The Workshop and Conference will be a gathering of the global community of innovators and pioneers in the fields of architecture, design and engineering.
The event will be in two parts: a four day Workshop 19-22 March, and a public conference beginning with Talkshop 23 March, followed by a Symposium 24 March. The event follows the format of the highly successful preceding events sg2010 Barcelona and sg2011 Copenhagen.
sg2012 Challenge Material Intensities
Simulation, Energy, Environment
Imagine the design space of architecture was no longer at the scale of rooms, walls and atria, but that of cells, grains and vapour droplets. Rather than the flow of people, services, or construction schedules, the focus becomes the flow of light, vapour, molecular vibrations and growth schedules: design from the inside out.
The sg2012 challenge, Material Intensities, is intended to dissolve our notion of the built environment as inert constructions enclosing physically sealed spaces. Spaces and boundaries are abundant with vibration, fluctuating intensities, shifting gradients and flows. The materials that define them are in a continual state of becoming: a dance of energy and information. Material potential is defined by multiple properties: acoustical, chemical, electrical, environmental, magnetic, manufacturing, mechanical, optical, radiological, sensorial, and thermal. The challenge for sg2012 Material Intensities is to consider material economy when creating environments, micro-climates and contexts congenial for social interaction, activities and organisation. This challenge calls for design innovation and dialogue between disciplines and responsibilities. sg2010 Working Prototypes strove to emancipate digital design from the hard drive by moving from the virtual to the actual in wrestling with the tangible world of physical fabrication. sg2011 Building the Invisible focused on informing digital design with real world data. sg2012 Material Intensities strives to energise our digital prototypes and infuse them with material behaviour. They have the potential to become rich simulations informed by the material dynamics, chemical composition, energy flows, force fields and environmental conditions that feed back into the design process.
More information can be found at http://www.smartgeometry.org
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Added by Shane Burger at 12:29pm on December 13, 2011
se (like in nature). the length of the sticks shall be controlled by the brightnessvalues of a picture. so the bend have to be controlled, too.
now we have several problems:
1. how can i map a hexgrid on a curved surface?
2. how can i adapt the grid to the dimensions of the surface (no overlap, no gaps to the bound)?
3. important
: to create the curved sticks, we use points on a line and we move some of them and then we want to connect the right points via interpolated curve to create each curved stick. now the problem is that the points have to been filtered in the right way. we know that we have to filter each list of points to the index values of the points. the number of index values is the number of hexgrid rows, so there are a lot and we can't use a list item for each one. it could be hundreds.
is there any opportunity to sort a list after the index values (first every index=0, then index=1, ...n)?
or is there any component which does a group of operations for n-times (n is the flexible number of index values) ?
4. how can i control the length and bend of the sticks via the brightnessvalues of a picture?
please help us. thanks.
german version:
In einem hexagonalen Raster soll sich senkrecht zu Oberfläche ein Stab im Mittelpunkt jedes Sechsecks befinden. Dieser soll sich ab einem gewissen (festgelegten) Punkt Richtung Boden biegen. Zusätzlich wird die Länge des Stabes zum Beispiel durch die Information eines Bildes gesteuert, so dass auch die Biegung, je nach Länge, geregelt werden muss.
Wir haben ein Hexagonales Grid (HexGrid) erzeugt und in jeden Mittelpunkt eine Linie senkrecht zum Grid erzeugt, aus der wir uns Punkte mit CurvePoint ausgeben lassen. Der letzte ist verschoben, um eine Biegung zu simulieren. Um die Punkte zu einer interpolierten Kurve zu verbinden, müssen sie nach dem Index sortiert werden. Gibt es eine andere Möglichkeit, als jeden einzelnen Indexwert über ein ListItem herauszufiltern (Da die Rasterung flexibel einstellbar sein soll, entstehen n Indexwerte)? Oder kann man eine Liste nach den Indexwerten, also nicht nach den Punkten, sortieren?
Und wie kann man über Bildhelligkeitswerte die Länge der Stäbe und damit auch die Biegung steuern (ein kurzer Stab biegt sich weniger als ein langer Stab)?
Gibt es die Möglichkeit ein hexagonales Raster auf eine gekrümmte Fläche zu mappen?
Und wie passt man ein solches Raster (HexGrid) in eine Fläche mit definierten Maßen ein, ohne dass das Raster an den Rändern übersteht oder die Fläche nicht vollkommen ausfüllt?
danke.…
Added by doro hamann at 7:34am on December 20, 2011
mplex the models are. If we are running multi-room E+ studies, that will take far longer to calculate.
Rhino/Grasshopper = <1%
Generating Radiance .ill files = 88%
Processing .ill files into DA, etc. = ~2%
E+ = 10%
Parallelizing Grasshopper:
My first instinct is to avoid this problem by running GH on one computer only. Creating the batch files is very fast. The trick will be sending the radiance and E+ batch files to multiple computers. Perhaps a “round-robin” approach could send each iteration to another node on the network until all iterations are assigned. I have no idea how to do that but hope that it is something that can be executed within grasshopper, perhaps a custom code module. I think GH can set a directory for Radiance and E+ to save all final files to. We can set this to a local server location so all runs output to the same location. It will likely run slower than it would on the C:drive, but those losses are acceptable if we can get parallelization to work.
I’m concerned about post-processing of the Radiance/E+ runs. For starters, Honeybee calculates DA after it runs the .ill files. This doesn’t take very long, but it is a separate process that is not included in the original Radiance batch file. Any other data manipulation we intend to automatically run in GH will be left out of the batch file as well. Consolidating the results into a format that Design Explorer or Pollination can read also takes a bit of post-processing. So, it seems to me that we may want to split up the GH automation as follows:
Initiate
Parametrically generate geometry
Assign input values, material, etc.
Generate radiance/ E+ batch files for all iterations
Calculate
Calc separate runs of Radiance/E+ in parallel via network clusters. Each run will be a unique iteration.
Save all temp files to single server location on server
Post Processing
Run a GH script from a single computer. Translate .ill files or .idf files into custom metrics or graphics (DA, ASE, %shade down, net solar gain, etc.)
Collect final data in single location (excel document) to be read by Design Explorer or Pollination.
The above workflow avoids having to parallelize GH. The consequence is that we can’t parallelize any post-processing routines. This may be easier to implement in the short term, but long term we should try to parallelize everything.
Parallelizing EnergyPlus/Radiance:
I agree that the best way to enable large numbers of iterations is to set up multiple unique runs of radiance and E+ on separate computers. I don’t see the incentive to split individual runs between multiple processors because the modular nature of the iterative parametric models does this for us. Multiple unique runs will simplify the post-processing as well.
It seems that the advantages of optimizing matrix based calculations (3-5 phase methods) are most beneficial when iterations are run in series. Is it possible for multiple iterations running on different CPUs to reference the same matrices stored in a common location? Will that enable parallel computation to also benefit from reusing pre-calculated information?
Clustering computers and GPU based calculations:
Clustering unused computers seems like a natural next step for us. Our IT guru told me that we need come kind of software to make this happen, but that he didn’t know what that would be. Do you know what Penn State uses? You mentioned it is a text-only Linux based system. Can you please elaborate so I can explain to our IT department?
Accelerad is a very exciting development, especially for rpict and annual glare analysis. I’m concerned that the high quality GPU’s required might limit our ability to implement it on a large scale within our office. Does it still work well on standard GPU’s? The computer cluster method can tap into resources we already have, which is a big advantage. Our current workflow uses image-based calcs sparingly, because grid-based simulations gather the critical information much faster. The major exception is glare. Accelerad would enable luminance-based glare metrics, especially annual glare metrics, to be more feasible within fast-paced projects. All of that is a good thing.
So, both clusters and GPU-based calcs are great steps forward. Combining both methods would be amazing, especially if it is further optimized by the computational methods you are working on.
Moving forward, I think I need to explore if/how GH can send iterations across a cluster network of some kind and see what it will take to implement Accelerad. I assume some custom scripting will be necessary.…
what they really mean by that, as in what buttons to push, so I assume it's a Windows Path entry?
2.) Modify PATH
Add the install location on the path, this is usually: C:\Program File\IronPython 2.7
But on 64-bit Windows systems it is: C:\Program File (x86)\IronPython 2.7
As a check, open a Windows command prompt and go to a directory (which is not the above) and type:
> ipy -V PythonContext 2.7.0.40 on .NET 4.0.30319.225
Tutorial on setting a Windows environmental variable (path):
http://www.computerhope.com/issues/ch000549.htm
But this fails to point out that path contains many entries already separated by semicolons so if I merely add a new variable called "path" it's likely that I will destroy existing program function. There's no info on how to just tack on another entry, and the Windows 7 edit box doesn't even show the whole collection, but one item (!), so I copied the existing path into a text editor to see the whole collection successfully and added the C:\Program Files (x86)\IronPython 2.7 entry after an added semicolon, correcting for an Enthought page typo of no 's' on the end of "Program Files". I also checked the others and many pointed to old missing directories so I deleted those entries.
...and the test fails and "ipy" is not recognized as a command, even though the path now shows up using "path" in the Windows CMD window, that is if I copy all by right clicking and pasting the stuff into a text editor to really view it all. I can run it from the source directory just fine.
The rabbit hole was indeed deep. Using the Task Manager (control-alt-delete) to kill Explorer and then Run in the menu to restart "Explorer," along with restarting the Windows CMD window however, worked. I can now invoke Iron Python ("ipy") via command line from any directory. For the "path" I edited path in the System Variables and not the User Variables. No, you don't have to type that whole crazy line above just to test the path variable, just "ipy" (and control-Z to quite IronPython) in the CMD window invoked by typing "cmd" into the Start menu search box.
From the CMD line this step did work fine:
3.) ironpkg
Bootstrap ironpkg, which is a package install manager for binary (egg based) Python packages. Download ironpkg-1.0.0.py and type:
> ipy ironpkg-1.0.0.py --install
Now the ironpkg command should be available:
> ironpkg -h(some useful help text is displayed here)
But of course Step 4 fails, giving pages of what seem to be error messages;
C:\Users\Nik>ironpkg scipy
Traceback (most recent call last):
File "C:\Program Files (x86)\IronPython 2.7\lib\site-packages\enstaller\utils.
py", line 92, in write_data_from_url
File "C:\Program Files (x86)\IronPython 2.7\Lib\urllib2.py", line 126, in urlo
pen
File "C:\Program Files (x86)\IronPython 2.7\Lib\urllib2.py", line 397, in open
File "C:\Program Files (x86)\IronPython 2.7\Lib\urllib2.py", line 509, in http
_response
...
Why can't I just download Numpy as a normal file and thus also have it easy for other users to install it when they use my scripts? This is just crazy and lazy. The Enthought developer has turned this into a computer game, with a missing registration link and then the last step spits out errors with utterly no information on how to fix it manually.
This Step 4 error is covered here:
http://discourse.mcneel.com/t/trying-to-import-numpy-in-rhino-python-but-im-getting-this-error-cannot-import-multiarray-from-numpy-core/12912/16…
Added by Nik Willmore at 2:36pm on October 11, 2015
Introduction to Grasshopper Videos by David Rutten.
Wondering how to get started with Grasshopper? Look no further. Spend an some time with the creator of Grasshopper, David Rutten, to learn the
levator over the automobile, complex issues are at play in concentrating population and built infrastructure in contemporary high-rise cities. How do you meet the challenges of system design for high quality compact urban environments?
The Smartgeometry Workshop is a unique creative cauldron attracting attendees from across the world of academia, professional practice and industry. The workshop is open to 100 applicants who come together for four intensive days of design and collaboration.
More Info and to Apply
The application deadline to attend the sg2014 Workshop has been extended to June 1st, 2014 at midnight PST. Reviews and early notifications will proceed for those who have already applied.
Image: Cities without Ground - Adam Frampton, Jonathan D Solomon and Clara Wong
WORKSHOP CLUSTERS
The sg2014 Workshop will be organised around Clusters. Clusters are hubs of expertise. They comprise of people, knowledge, tools, materials and machines. The Clusters provide a focus for workshop participants working together within a common framework.
Clusters provide a forum for the exchange of ideas, processes and techniques and act as a catalyst for design resolution. The sg2014 Workshop is made up of ten Clusters that respond in diverse ways to the challenge Urban Compaction.
sg2014 WORKSHOP CLUSTERS
The Bearable Lightness of Being
Block
Deep Space
Design Space Exploration
Flows, Bits, Relationships
Fulldome Projections
HK_smarTowers
Private Microclimates
Resilient Networks
Spaces in Experience
CONFERENCE
After four intense days of innovative work, the 2-day sgConference offers an opportunity for critical reflection on what has been accomplished in the Workshop and in the global design arena. It will be an opportunity to open debates, pose questions, challenge orthodoxies, and propose new ideas.
The sgConference features invited keynote speakers showcasing major projects and research from around the globe, mixed with panel sessions for open debate. The end of the first day will include reports and highlights from the Workshop, giving an opportunity to view work created during the previous four days of intensive collaboration, design and development, followed by an exhibition of the work.
Invited Speakers & Panelists:Carlo Ratti Sensable City Lab, MITCristiano Ceccato Zaha HadidTom Kvan & Justyna Karakiewicz Melbourne UniversityJun Sato Jun Sato Structural EngineersMario Carpo Yale UniversityEddie Can Zaha HadidLi Xinggang Atelier Li Xinggang, China Architecture Design & Research GroupMartin Reise FrontPhilip Yuan Tongji ShanghaiYusuke Obuchi Tokyo UniversityYusushi Ikada Ikada-Lab Keio University, Japan
Additional speakers to be announced soon. Registration to open soon.
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lly it should not make much of a difference - random number generation is not affected, mutation also is not. crossover is a bit more tricky, I use Simulated Binary Crossover (SBX-20) which was introduced already in 1194:
Deb K., Agrawal R. B.: Simulated Binary Crossover for Continuous Search Space, inIITK/ME/SMD-94027, Convenor, Technical Reports, Indian Institue of Technology, Kanpur, India,November 1994
Abst ract. The success of binary-coded gene t ic algorithms (GA s) inproblems having discrete sear ch sp ace largely depends on the codingused to represent the prob lem variables and on the crossover ope ratorthat propagates buildin g blocks from pare nt strings to childrenst rings . In solving optimization problems having continuous searchspace, binary-co ded GAs discr et ize the search space by using a codingof the problem var iables in binary st rings. However , t he coding of realvaluedvari ables in finit e-length st rings causes a number of difficulties:inability to achieve arbit rary pr ecision in the obtained solution , fixedmapping of problem var iab les, inh eren t Hamming cliff problem associatedwit h binary coding, and processing of Holland 's schemata incont inuous search space. Although a number of real-coded GAs aredevelop ed to solve optimization problems having a cont inuous searchspace, the search powers of these crossover operators are not adequate .In t his paper , t he search power of a crossover operator is defined int erms of the probability of creating an arbitrary child solut ion froma given pair of parent solutions . Motivated by t he success of binarycodedGAs in discret e search space problems , we develop a real-codedcrossover (which we call the simulated binar y crossover , or SBX) operatorwhose search power is similar to that of the single-point crossoverused in binary-coded GAs . Simulation results on a number of realvaluedt est problems of varying difficulty and dimensionality suggestt hat the real-cod ed GAs with t he SBX operator ar e ab le to perform asgood or bet t er than binary-cod ed GAs wit h t he single-po int crossover.SBX is found to be particularly useful in problems having mult ip le optimalsolutions with a narrow global basin an d in prob lems where thelower and upper bo unds of the global optimum are not known a priori.Further , a simulation on a two-var iable blocked function showsthat the real-coded GA with SBX work s as suggested by Goldberg
and in most cases t he performance of real-coded GA with SBX is similarto that of binary GAs with a single-point crossover. Based onth ese encouraging results, this paper suggests a number of extensionsto the present study.
7. ConclusionsIn this paper, a real-coded crossover operator has been develop ed bas ed ont he search characte rist ics of a single-point crossover used in binary -codedGAs. In ord er to define the search power of a crossover operator, a spreadfactor has been introduced as the ratio of the absolute differences of thechildren points to that of the parent points. Thereaft er , the probabilityof creat ing a child point for two given parent points has been derived forthe single-point crossover. Motivat ed by the success of binary-coded GAsin problems wit h discrete sear ch space, a simul ated bin ary crossover (SBX)operator has been develop ed to solve problems having cont inuous searchspace. The SBX operator has search power similar to that of the single-po intcrossover.On a number of t est fun ctions, including De Jong's five te st fun ct ions, ithas been found that real-coded GAs with the SBX operator can overcome anumb er of difficult ies inherent with binary-coded GAs in solving cont inuoussearch space problems-Hamming cliff problem, arbitrary pr ecision problem,and fixed mapped coding problem. In the comparison of real-coded GAs wit ha SBX operator and binary-coded GAs with a single-point crossover ope rat or ,it has been observed that the performance of the former is better than thelatt er on continuous functions and the performance of the former is similarto the lat ter in solving discret e and difficult functions. In comparison withanother real-coded crossover operator (i.e. , BLX-0 .5) suggested elsewhere ,SBX performs better in difficult test functions. It has also been observedthat SBX is particularly useful in problems where the bounds of the optimum
point is not known a priori and wher e there are multi ple optima, of whichone is global.Real-coded GAs wit h t he SBX op erator have also been tried in solvinga two-variab le blocked function (the concept of blocked fun ctions was introducedin [10]). Blocked fun ct ions are difficult for real-coded GAs , becauselocal optimal points block t he progress of search to continue towards t heglobal optimal point . The simulat ion results on t he two-var iable blockedfunction have shown that in most occasions , the sea rch proceeds the way aspr edicted in [10]. Most importantly, it has been observed that the real-codedGAs wit h SBX work similar to that of t he binary-coded GAs wit h single-pointcrossover in overcoming t he barrier of the local peaks and converging to t heglobal bas in. However , it is premature to conclude whether real-coded GAswit h SBX op erator can overcome t he local barriers in higher-dimensionalblocked fun ct ions.These results are encour aging and suggest avenues for further research.Because the SBX ope rat or uses a probability distribut ion for choosing a childpo int , the real-coded GAs wit h SBX are one st ep ahead of the binary-codedGAs in te rms of ach ieving a convergence proof for GAs. With a direct probabilist ic relationship between children and parent points used in t his paper,cues from t he clas sical stochast ic optimization methods can be borrowed toachieve a convergence proof of GAs , or a much closer tie between the classicaloptimization methods and GAs is on t he horizon.
In short, according to the authors my SBX operator using real gene values is as good as older ones specially designed for discrete searches, and better in continuous searches. SBX as far as i know meanwhile is a standard general crossover operator.
But:
- there might be better ones out there i just havent seen yet. please tell me.
- besides tournament selection and mutation, crossover is just one part of the breeding pipeline. also there is the elite management for MOEA which is AT LEAST as important as the breeding itself.
- depending on the problem, there are almost always better specific ways of how to code the mutation and the crossover operators. but octopus is meant to keep it general for the moment - maybe there's a way for an interface to code those things yourself..!?
2) elite size = SPEA-2 archive size, yes. the rate depends on your convergence behaviour i would say. i usually start off with at least half the size of the population, but mostly the same size (as it is hard-coded in the new version, i just realize) is big enough.
4) the non-dominated front is always put into the archive first. if the archive size is exceeded, the least important individual (the significant strategy in SPEA-2) are truncated one by one until the size is reached. if it is smaller, the fittest dominated individuals are put into the elite. the latter happens in the beginning of the run, when the front wasn't discovered well yet.
3) yes it is. this is a custom implementation i figured out myself. however i'm close to have the HypE algorithm working in the new version, which natively has got the possibility to articulate perference relations on sets of solutions.
…
This blog post is a rough approximation of the lecture I gave at the AAG10 conference in Vienna on September 21st 2010. Naturally it will be quite a different experience as the medium is quite…
Added by David Rutten at 3:27pm on September 24, 2010