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1),"\n",(0,l.jsx)(t.h3,{id:"background",children:"Background"}),"\n",(0,l.jsx)(t.p,{children:"In 3D printing, several parameters can significantly affect the mechanical properties of\nthe printed object:"}),"\n",(0,l.jsxs)(t.ul,{children:["\n",(0,l.jsxs)(t.li,{children:["\n",(0,l.jsxs)(t.p,{children:[(0,l.jsx)(t.strong,{children:"Infill Density"}),": The percentage of material used inside the object. Higher infill\ndensity generally increases strength but also weight and material usage."]}),"\n"]}),"\n",(0,l.jsxs)(t.li,{children:["\n",(0,l.jsxs)(t.p,{children:[(0,l.jsx)(t.strong,{children:"Layer Height"}),": The thickness of each layer of material. Smaller layer heights can\nimprove surface finish and detail but increase print time."]}),"\n"]}),"\n",(0,l.jsxs)(t.li,{children:["\n",(0,l.jsxs)(t.p,{children:[(0,l.jsx)(t.strong,{children:"Infill Type"}),": The pattern used to fill the interior of the object. Different\npatterns (e.g., honeycomb, gyroid, lines, rectilinear) offer various balances of\nstrength, speed, and material efficiency."]}),"\n"]}),"\n",(0,l.jsxs)(t.li,{children:["\n",(0,l.jsxs)(t.p,{children:[(0,l.jsx)(t.strong,{children:"Strength Measurement"}),": In this tutorial, we assume the strength of the 3D printed\npart is measured using compression testing, which evaluates how the object performs\nunder compressive stress."]}),"\n"]}),"\n"]}),"\n",(0,l.jsx)(t.h3,{id:"learning-objectives",children:"Learning Objectives"}),"\n",(0,l.jsxs)(t.ul,{children:["\n",(0,l.jsx)(t.li,{children:"Understand black box optimization concepts"}),"\n",(0,l.jsx)(t.li,{children:"Define an optimization problem using Ax"}),"\n",(0,l.jsxs)(t.li,{children:["Configure and run an experiment using Ax's ",(0,l.jsx)(t.code,{children:"Client"})]}),"\n",(0,l.jsx)(t.li,{children:"Analyze the results of the optimization"}),"\n"]}),"\n",(0,l.jsx)(t.h3,{id:"prerequisites",children:"Prerequisites"}),"\n",(0,l.jsxs)(t.ul,{children:["\n",(0,l.jsx)(t.li,{children:"Familiarity with Python and basic programming concepts"}),"\n",(0,l.jsxs)(t.li,{children:["Understanding of ",(0,l.jsx)(t.a,{href:"/docs/1.0.0/intro-to-ae",children:"adaptive experimentation"})," and\n",(0,l.jsx)(t.a,{href:"/docs/1.0.0/intro-to-bo",children:"Bayesian optimization"})]}),"\n"]}),"\n",(0,l.jsx)(t.h2,{id:"step-1-import-necessary-modules",children:"Step 1: Import Necessary Modules"}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:"from ax.api.client import Client\nfrom ax.api.configs import RangeParameterConfig, ChoiceParameterConfig\n"})}),"\n",(0,l.jsx)(t.h2,{id:"step-2-initialize-client",children:"Step 2: Initialize Client"}),"\n",(0,l.jsxs)(t.p,{children:["Create an instance of the ",(0,l.jsx)(t.code,{children:"Client"})," to manage the state of your experiment."]}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:"client = Client()\n"})}),"\n",(0,l.jsx)(t.h2,{id:"step-3-configure-experiment",children:"Step 3: Configure Experiment"}),"\n",(0,l.jsxs)(t.p,{children:["Define the parameters for the 3D printing optimization problem. The infill density and\nlayer height can take on any value within their respective bounds so we will configure\nboth using ",(0,l.jsx)(t.code,{children:"RangeParameterConfig"}),'s. On the other hand, infill type be either have one of\nfour distinct values: "honeycomb", "gyroid", "lines", or "rectilinear". We will use a\n',(0,l.jsx)(t.code,{children:"ChoiceParameterConfig"})," to represent it in the optimization."]}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:'infill_density = RangeParameterConfig(name="infill_density", parameter_type="float", bounds=(0, 100))\nlayer_height = RangeParameterConfig(name="layer_height", parameter_type="float", bounds=(0.1, 0.4))\ninfill_type = ChoiceParameterConfig(name="infill_type", parameter_type="str", values=["honeycomb", "gyroid", "lines", "rectilinear"])\n\nclient.configure_experiment(\n parameters=[infill_density, layer_height, infill_type],\n # The following arguments are only necessary when saving to the DB\n name="3d_print_strength_experiment",\n description="Maximize strength of 3D printed parts",\n owner="developer",\n)\n'})}),"\n",(0,l.jsx)(n.A,{children:'/home/runner/work/Ax/Ax/ax/api/utils/instantiation/from_config.py:75: AxParameterWarning: is_ordered is not specified for ChoiceParameter "infill_type". 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However, we know that modifying the infill density, layer\nheight, and infill type will affect the weight of the part as well. We'll include a\nrequirement that the part must not weigh more than 10 grams by setting an outcome\nconstraint when we call ",(0,l.jsx)(t.code,{children:"configure_experiment"}),"."]}),"\n",(0,l.jsxs)(t.p,{children:["The following code will tell the ",(0,l.jsx)(t.code,{children:"Client"})," that we intend to maximize compressive\nstrength while keeping the weight less than 10 grams."]}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:'client.configure_optimization(objective="compressive_strength", outcome_constraints=["weight <= 10"])\n'})}),"\n",(0,l.jsx)(t.h2,{id:"step-5-run-trials",children:"Step 5: Run Trials"}),"\n",(0,l.jsxs)(t.p,{children:["Now the ",(0,l.jsx)(t.code,{children:"Client"})," has been configured we can begin conducting the experiment. Use\n",(0,l.jsx)(t.code,{children:"attach_trial"})," to attach any existing data, use ",(0,l.jsx)(t.code,{children:"get_next_trials"})," to generate parameter\nsuggestions, and use ",(0,l.jsx)(t.code,{children:"complete_trial"})," to report manually observed results."]}),"\n",(0,l.jsx)(t.h3,{id:"attach-preexisting-trials",children:"Attach Preexisting Trials"}),"\n",(0,l.jsx)(t.p,{children:'Sometimes in our optimization experiments we may already have some previously collected\ndata from manual "trials" conducted before the Ax experiment began. This can be\nincredibly useful! If we attach this data as custom trials, Ax will be able to use the\ndata points in its optimization algorithm and improve performance.'}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:'# Pairs of previously evaluated parameterizations and associated metric readings\npreexisting_trials = [\n (\n {"infill_density": 10.43, "layer_height": 0.3, "infill_type": "gyroid"},\n {"compressive_strength": 1.74, "weight": 0.52},\n ),\n (\n {"infill_density": 55.54, "layer_height": 0.12, "infill_type": "lines"},\n {"compressive_strength": 4.63, "weight": 2.31},\n ),\n (\n {"infill_density": 99.43, "layer_height": 0.35, "infill_type": "rectilinear"},\n {"compressive_strength": 5.68, "weight": 2.84},\n ),\n (\n {"infill_density": 41.44, "layer_height": 0.21, "infill_type": "rectilinear"},\n {"compressive_strength": 3.95, "weight": 1.97},\n ),\n (\n {"infill_density": 27.23, "layer_height": 0.37, "infill_type": "honeycomb"},\n {"compressive_strength": 7.36, "weight": 3.31},\n ),\n (\n {"infill_density": 33.57, "layer_height": 0.24, "infill_type": "honeycomb"},\n {"compressive_strength": 13.99, "weight": 6.29},\n ),\n]\n\nfor parameters, data in preexisting_trials:\n # Attach the parameterization to the Client as a trial and immediately complete it with the preexisting data\n trial_index = client.attach_trial(parameters=parameters)\n client.complete_trial(trial_index=trial_index, raw_data=data)\n'})}),"\n",(0,l.jsx)(t.h3,{id:"ask-for-trials",children:"Ask for trials"}),"\n",(0,l.jsxs)(t.p,{children:["Now, let's have Ax suggest which trials to evaluate so that we can find the optimal\nconfiguration more efficiently. We'll do this by calling ",(0,l.jsx)(t.code,{children:"get_next_trials"}),". We'll make\nuse of Ax's support for parallelism, i.e. suggesting more than one trial at a time --\nthis can allow us to conduct our experiment much faster! If our lab had three identical\n3D printers, we could ask Ax for a batch of three trials and evaluate three different\ninfill density, layer height, and infill types at once."]}),"\n",(0,l.jsx)(t.p,{children:'Note that there will always be a tradeoff between "parallelism" and optimization\nperformance since the quality of a suggested trial is often proportional to the amount\nof data Ax has access to.'}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:"trials = client.get_next_trials(max_trials=3)\ntrials\n"})}),"\n",(0,l.jsx)(n.A,{children:"{6: {'infill_density': 50.0, 'layer_height': 0.25, 'infill_type': 'lines'},\n7: {'infill_density': 63.66994380950928,\n'layer_height': 0.18139435052871705,\n'infill_type': 'gyroid'},\n8: {'infill_density': 55.5455832778283,\n'layer_height': 0.2043214845070208,\n'infill_type': 'honeycomb'}}"}),"\n",(0,l.jsx)(t.h3,{id:"tell-ax-the-results",children:"Tell Ax the results"}),"\n",(0,l.jsxs)(t.p,{children:["In a real-world scenerio we would print parts using the three suggested\nparameterizations and measure the compressive strength and weight manually, though in\nthis tutorial we will simulate by calling a function. Once the data is collected we will\ntell Ax the result by calling ",(0,l.jsx)(t.code,{children:"complete_trial"}),"."]}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:'def evaluate(\n infill_density: float, layer_height: float, infill_type: str\n) -> dict[str, float]:\n strength_map = {"lines": 1, "rectilinear": 2, "gyroid": 5, "honeycomb": 10}\n weight_map = {"lines": 1, "rectilinear": 2, "gyroid": 3, "honeycomb": 9}\n\n return {\n "compressive_strength": (\n infill_density / layer_height * strength_map[infill_type]\n )\n / 100,\n "weight": (infill_density / layer_height * weight_map[infill_type]) / 200,\n }\n\n\nfor trial_index, parameters in trials.items():\n client.complete_trial(trial_index=trial_index, raw_data=evaluate(**parameters))\n'})}),"\n",(0,l.jsx)(t.p,{children:"We'll repeat this process a number of times. Typically experimentation will continue\nuntil a satisfactory combination has been found, experimentation resources (in this\nexample our 3D printing filliment) have been exhausted, or we feel we have spent enough\ntime on optimization."}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:"# Ask Ax for the next trials\ntrials = client.get_next_trials(max_trials=3)\ntrials\n"})}),"\n",(0,l.jsx)(n.A,{children:"{9: {'infill_density': 42.75147562326184,\n'layer_height': 0.123463764124029,\n'infill_type': 'honeycomb'},\n10: {'infill_density': 72.76573270625342,\n'layer_height': 0.2234125495505756,\n'infill_type': 'honeycomb'},\n11: {'infill_density': 45.944164543600095,\n'layer_height': 0.1,\n'infill_type': 'honeycomb'}}"}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:"# Tell Ax the result of those trials\nfor trial_index, parameters in trials.items():\n client.complete_trial(trial_index=trial_index, raw_data=evaluate(**parameters))\n"})}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:"# Ask Ax for the next trials\ntrials = client.get_next_trials(max_trials=3)\ntrials\n"})}),"\n",(0,l.jsx)(n.A,{children:"{12: {'infill_density': 0.0,\n'layer_height': 0.38979593229689674,\n'infill_type': 'lines'},\n13: {'infill_density': 100.0,\n'layer_height': 0.39679420439370955,\n'infill_type': 'honeycomb'},\n14: {'infill_density': 15.554641847623298,\n'layer_height': 0.33463630813484546,\n'infill_type': 'rectilinear'}}"}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:"# Tell Ax the result of those trials\nfor trial_index, parameters in trials.items():\n client.complete_trial(trial_index=trial_index, raw_data=evaluate(**parameters))\n"})}),"\n",(0,l.jsx)(t.h2,{id:"step-6-analyze-results",children:"Step 6: Analyze Results"}),"\n",(0,l.jsxs)(t.p,{children:["At any time during the experiment you may analyze the results of the experiment. Most\ncommonly this means extracting the parameterization from the best performing trial you\nconducted. The best trial will have the optimal objective value ",(0,l.jsx)(t.strong,{children:"without violating any\noutcome constraints"}),"."]}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:'best_parameters, prediction, index, name = client.get_best_parameterization()\nprint("Best Parameters:", best_parameters)\nprint("Prediction (mean, variance):", prediction)\n'})}),"\n",(0,l.jsx)(n.A,{children:"Best Parameters: {'infill_density': 63.66994380950928, 'layer_height': 0.18139435052871705, 'infill_type': 'gyroid'}\nPrediction (mean, variance): {'weight': (np.float64(5.241676593341699), np.float64(0.5914311886320532)), 'compressive_strength': (np.float64(17.626494301644186), np.float64(5.039528566546781))}"}),"\n",(0,l.jsx)(t.h2,{id:"step-7-compute-analyses",children:"Step 7: Compute Analyses"}),"\n",(0,l.jsxs)(t.p,{children:["Ax can also produce a number of analyses to help interpret the results of the experiment\nvia ",(0,l.jsx)(t.code,{children:"client.compute_analyses"}
1),". Users can manually select which analyses to run, or can\nallow Ax to select which would be most relevant. In this case Ax selects the following:"]}),"\n",(0,l.jsxs)(t.ul,{children:["\n",(0,l.jsxs)(t.li,{children:[(0,l.jsx)(t.strong,{children:"Parrellel Coordinates Plot"})," shows which parameterizations were evaluated and what\nmetric values were observed -- this is useful for getting a high level overview of how\nthoroughly the search space was explored and which regions tend to produce which\noutcomes"]}),"\n",(0,l.jsxs)(t.li,{children:[(0,l.jsx)(t.strong,{children:"Scatter Plot"})," shows the effects of each trial on two metrics, and is useful for\nunderstanding the trade-off between the two outcomes"]}),"\n",(0,l.jsxs)(t.li,{children:[(0,l.jsx)(t.strong,{children:"Sensitivity Analysis Plot"})," shows which parameters have the largest affect on the\nobjective using\n",(0,l.jsx)(t.a,{href:"https://en.wikipedia.org/wiki/Variance-based_sensitivity_analysis",children:"Sobol Indicies"})]}),"\n",(0,l.jsxs)(t.li,{children:[(0,l.jsx)(t.strong,{children:"Slice Plot"})," shows how the model predicts a single parameter effects the objective\nalong with a confidence interval"]}),"\n",(0,l.jsxs)(t.li,{children:[(0,l.jsx)(t.strong,{children:"Contour Plot"})," shows how the model predicts a pair of parameters effects the\nobjective as a 2D surface"]}),"\n",(0,l.jsxs)(t.li,{children:[(0,l.jsx)(t.strong,{children:"Summary"})," lists all trials generated along with their parameterizations,\nobservations, and miscellaneous metadata"]}),"\n",(0,l.jsxs)(t.li,{children:[(0,l.jsx)(t.strong,{children:"Cross Validation"})," helps to visualize how well the surrogate model is able to\npredict out of sample points"]}),"\n"]}),"\n",(0,l.jsx)(t.pre,{children:(0,l.jsx)(t.code,{className:"language-python",children:"# display=True instructs Ax to sort then render the resulting analyses\ncards = client.compute_analyses(display=True)\n"})}),"\n",(0,l.jsx)(t.p,{children:(0,l.jsx)(t.strong,{children:"Modeled compressive_strength vs. weight"})}),"\n",(0,l.jsx)(t.p,{children:"This plot displays the effects of each arm on the two selected metrics. It is useful for\nunderstanding the trade-off between the two metrics and for visualizing the Pareto\nfrontier."}),"\n",(0,l.jsx)(s.z,{data:r(89745)}),"\n",(0,l.jsx)(t.p,{children:(0,l.jsx)(t.strong,{children:"Parallel Coordinates for compressive_strength"})}),"\n",(0,l.jsx)(t.p,{children:"The parallel coordinates plot displays multi-dimensional data by representing each\nparameter as a parallel axis. This plot helps in asse
1ssing how thoroughly the search\nspace has been explored and in identifying patterns or clusterings associated with\nhigh-performing (good) or low-performing (bad) arms. By tracing lines across the axes,\none can observe correlations and interactions between parameters, gaining insights into\nthe relationships that contribute to the success or failure of different configurations\nwithin the experiment."}),"\n",(0,l.jsx)(s.z,{data:r(81612)}),"\n",(0,l.jsx)(t.p,{children:(0,l.jsx)(t.strong,{children:"Summary for 3d_print_strength_experiment"})}),"\n",(0,l.jsxs)(t.p,{children:["High-level summary of the ",(0,l.jsx)(t.code,{children:"Trial"}),"-s in this ",(0,l.jsx)(t.code,{children:"Experiment"})]}),"\n",(0,l.jsxs)(t.table,{children:[(0,l.jsx)(t.thead,{children:(0,l.jsxs)(t.tr,{children:[(0,l.jsx)(t.th,{style:{textAlign:"right"}}),(0,l.jsx)(t.th,{style:{textAlign:"right"},children:"trial_index"}),(0,l.jsx)(t.th,{style:{textAlign:"right"},children:"arm_name"}),(0,l.jsx)(t.th,{style:{textAlign:"left"},children:"trial_status"}),(0,l.jsx)(t.th,{style:{textAlign:"left"},children:"generation_node"}),(0,l.jsx)(t.th,{style:{textAlign:"right"},children:"weight"}),(0,l.jsx)(t.th,{style:{textAlign:"right"},children:"compressive_strength"}),(0,l.jsx)(t.th,{style:{textAlign:"right"},children:"infill_density"}),(0,l.jsx)(t.th,{style:{textAlign:"right"},children:"layer_height"}),(0,l.jsx)(t.th,{style:{textAlign:"left"},children:"infill_type"})]})}),(0,l.jsxs)(t.tbody,{children:[(0,l.jsxs)(t.tr,{children:[(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"0"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"0"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"0_0"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"COMPLETED"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"nan"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"0.52"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"1.74"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"10.43"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"0.3"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"gyroid"})]}),(0,l.jsxs)(t.tr,{children:[(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"1"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"1"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"1_0"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"COMPLETED"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"nan"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"2.31"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"4.63"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"55.54"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"0.12"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"lines"})]}),(0,l.jsxs)(t.tr,{children:[(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"2"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"2"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"2_0"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"COMPLETED"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"nan"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"2.84"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"5.68"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"99.43"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"0.35"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"rectilinear"})]}),(0,l.jsxs)(t.tr,{children:[(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"3"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"3"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"3_0"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"COMPLETED"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"nan"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"1.97"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"3.95"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"41.44"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"0.21"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"rectilinear"})]}),(0,l.jsxs)(t.tr,{children:[(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"4"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"4"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"4_0"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"COMPLETED"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"nan"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"3.31"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"7.36"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"27.23"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"0.37"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"honeycomb"})]}),(0,l.jsxs)(t.tr,{children:[(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"5"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"5"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"5_0"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"COMPLETED"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"nan"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"6.29"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"13.99"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"33.57"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"0.24"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"honeycomb"})]}),(0,l.jsxs)(t.tr,{children:[(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"6"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"6"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"6_0"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"COMPLETED"}),(0,l.jsx)(t.td,{style:{textAlign:"left"},children:"CenterOfSearchSpace"}),(0,l.jsx)(t.td,{style:{textAlign:"right"},children:"1"}),(0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Analysis for c
1ompressive_strength"})}),"\n",(0,l.jsx)(t.p,{children:"Understand how each parameter affects compressive_strength according to a second-order\nsensitivity analysis."}),"\n",(0,l.jsx)(s.z,{data:r(5426)}),"\n",(0,l.jsx)(t.p,{children:(0,l.jsx)(t.strong,{children:"layer_height vs. compressive_strength"})}),"\n",(0,l.jsx)(t.p,{children:"The slice plot provides a one-dimensional view of predicted outcomes for\ncompressive_strength as a function of a single parameter, while keeping all other\nparameters fixed at their status_quo value (or mean value if status_quo is unavailable).\nThis visualization helps in understanding the sensitivity and impact of changes in the\nselected parameter on the predicted metric outcomes."}),"\n",(0,l.jsx)(s.z,{data:r(91139)}),"\n",(0,l.jsx)(t.p,{children:(0,l.jsx)(t.strong,{children:"infill_density vs. compressive_strength"})}),"\n",(0,l.jsx)(t.p,{children:"The slice plot provides a one-dimensional view of predicted outcomes for\ncompressive_strength as a function of a single parameter, while keeping all other\nparameters fixed at their status_quo value (or mean value if status_quo is unavailable).\nThis visualization helps in understanding the sensitivity and impact of changes in the\nselected parameter on the predicted metric outcomes."}),"\n",(0,l.jsx)(s.z,{data:r(76727)}),"\n",(0,l.jsx)(t.p,{children:(0,l.jsx)(t.strong,{children:"infill_density, layer_height vs. compressive_strength"})}),"\n",(0,l.jsx)(t.p,{children:"The contour plot visualizes the predicted outcomes for compressive_strength across a\ntwo-dimensional parameter space, with other parameters held fixed at their status_quo\nvalue (or mean value if status_quo is unavailable). 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