/saVVy-v01a

saVVy follows the 41 steps of the All-i framework, a complete conceptual model of an advanced aerospace vehicle such as an EVTOL, CAV, multicopter, E-Thrust, or Blended Wing Body (BWB) can be designed and deployed onto the market from a 2D sketch.

saVVy-v01a

saVVy follows the 41 steps of the All-i framework, a complete conceptual model of an advanced aerospace vehicle such as an EVTOL, CAV, multicopter, E-Thrust, or Blended Wing Body (BWB) can be designed and deployed onto the market from a 2D sketch.

This Python code will utilize the All-i framework to create a conceptual model of the Savvy Chapter API features advance aerospace vehicle which are linked to feature product branch API s communicated through product branch APIs fetaured on Github pluuged into open source platform, eanbled to design CAV, BWB, EVTOL, E-Thrust , Mullticopters, all are linked and empopwered by saVVy , on a Tensor Flow model from a 2D sketch, leveraging advanced technology and robotics. The All-i framework offers a reliable platform for the fast development, automated products testing and validation, optimization feedback loops, and machine learning algorithms that generate insights for decision making.

The Python code written here uses Tensor Flow to create a model that can feature saVVY advanced aerospace concept design model. The code first breaks down user inputs into individual features that are then converted into input vectors, which are processed by a Savvy Chapter API via a tf.keras.Sequential model. Autopilot is used to automate the pre-processing, normalizing, batching, and training of the model. The model is validated and iterated on, and linked to a main CNN model. A 2D sketch feature is added to the model, followed by updating the model and compiling it. The model is evaluated and used to design advanced vehicles, and its performance is monitored and updated throughout the process. Finally, the saVVy Tensor Flow Model is finalized once the model is trained and optimized.

######################################################################### saVVy is an advanced analytics platform designed to leverage automation, machine learning, and artificial intelligence to develop better manufacturing solutions for the aerospace industry. It offers a comprehensive suite of tools and applications that allow users to quickly and easily expand their knowledge and understanding of the industry. saVVy utilizes comprehensive datasets, 3D models, and predictive analytics to help users make informed decisions that can lead to better business results.

a Python code to create a Tensor Flow model in 25 steps going through each step of the work packages to feature advanced aerospace vehicle concept designs on Tensor Flow models, enabled by saVVy Chapter API. This involves breaking down user inputs into individual features which are then converted into input vectors and processed by a Savvy Chapter API via a tf.keras.Sequential model. Autopilot is used to automate pre-processing, normalizing, batching, and training. The model is validated and iterated on, and linked to a main CNN model which is then used to generate datasets. A 2D Sketch feature is added to the model, followed by updating the model and compiling it. The model is evaluated and used to design advanced aerospace vehicles. Finally, the model is monitored and updated and the saVVy Tensor Flow Model is finalize

The input to the Python code to design saVVy on Tensor Flow model is a set of user inputs, which can be numerical, text, or image data. The user inputs will be broken down into individual features to be converted into input vectors that will then be processed by a Savvy Chapter API via a tf.keras.Sequential model.

The output of this Python code is a saVVy Tensor Flow model that can be used to evaluate and design advanced aerospace vehicle concepts. The model is composed of various layers of neurons interconnected through weights, with the weights determined by a process of training and optimization. The model is also validated and iterated on, and linked to a main CNN model. The model can be used to generate datasets and make predictions on advanced aerospace concept design models

Libraries and Packages Used

import tensorflow as tf import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split #########################################################################

The saVVy design on Tensor Flow model can be broken down into 25 steps:

  1. Break down user inputs into individual features
  2. Convert features to input vectors
  3. Process input vectors with a Savvy Chapter API
  4. Pre-process, normalize, batch, and train input vectors
  5. Validate and iterate on the model
  6. Link the model to a main CNN model
  7. Generate datasets
  8. Design, develop, update, optimize, and validate the digital twin
  9. Monitor and replicate actual conditions in the field with the digital twin
  10. Automate the process of pre-processing, normalizing, batching, and training
  11. Access data and run simulations
  12. Monitor and adjust
  13. Update the design for optimization and product approval
  14. Create a 2D sketch feature
  15. Update the model
  16. Compile the model
  17. Monitor and update the model
  18. Validate and test advance aerospace vehicles
  19. Generate datasets
  20. Evaluate the advance aircrafts
  21. Monitor and replicate actual conditions with the digital twin
  22. Update the model
  23. Compile the model
  24. Monitor and update the model
  25. Finalize saVVy on Tensor Flow model

Step 1: Input data from Git Hub api

Load GitHub repository containing advance aerospace vehicle concepts

git_repository = pd.read_csv('github_repo.csv')

Step 2: Breakdown user inputs into individual features

Extract input features from user input

input_features = git_repository.columns[0:]

Step 3: Generate input vectors from individual features

Scale the input to [-1,1]

input_vectors = tf.convert_to_tensor(git_repository, dtype=tf.float64) input_vectors = tf.divide(input_vectors, tf.abs(input_vectors))

Step 4: Create a Savvy Chapter API feature product using script

Create a Savvy Chapter API model from the user input

savvy_api =

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Create tf.keras.Sequential model

model = tf.keras.Sequential()

Step 5: Add model layers

Add first layers

model.add(tf.keras.layers.Dense(128,activation='relu', input_dim = len(input_features)))

Add second layer

model.add(tf.keras.layers.Dense(64,activation='relu'))

Add output layer

model.add(tf.keras.layers.Dense(len(savvy_api.features), activation='softmax'))

Step 6: Compile the model

Compile the model with the loss and optimizer

model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])

Step 7: Use of Autopilot for automated preprocessing, normalizing, batching, and training

Preprocessing input

preprocessed_input = np.reshape(input_vectors, (input_vectors.shape[0], input_vectors.shape[1], 1)) please

Use of Autopilot for automated preprocessing, normalizing, batching, and training

Preprocessing input

preprocessed_input = np.reshape(input_vectors, (input_vectors.shape[0], input_vectors.shape[1], 1))

Normalizing input

normalized_input = tf.keras.utils.normalize(preprocessed_input, axis=1)

Batching input

batched_input = tf.data.Dataset.from_tensor_slices(normalized_input).batch(128)

Training model

model.fit(batched_input, epochs=25)

Step 8: Validate and iterate on the Savvy API product design process

Evaluate the model

evaluation_results = model.evaluate(batched_input) print(evaluation_results)

Validate and iterate on the model

model.fit(batched_input, epochs=25)

Step 9: Interface the Savvy Chapter API to the master CNN model

master_api = tf.keras.Model(input

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Interface the Savvy Chapter API to the master CNN model

master_api = tf.keras.Model(inputs=model.input, outputs=[model.layers[1].model.output, model.layers[2].output])

Step 10: Generate datasets on Savvy with automated tools

Generate datasets on Savvy

datasets = master_api.predict(preprocessed_input)

Step 11: Design, develop, update, optimize, and validate the digital twin

Create train and test datasets

X_train, X_test, y_train, y_test = train_test_split(datasets[0], datasets[1], test_size=0.2, random_state=42)

Step 12: Monitor and replicate actual conditions in the field with the digital twin

Create a callback to monitor and replicate actual conditions in the field

class MonitorCallback(tf.keras.callbacks.Callback): def init(self, monitor_interval): self.monitor_interval = monitor_interval

def on_epoch_end(self,

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Create a callback to monitor and replicate actual conditions in the field

class MonitorCallback(tf.keras.callbacks.Callback): def init(self, monitor_interval): self.monitor_interval = monitor_interval

def on_epoch_end(self, epoch, logs=None): if ((epoch+1) % self.monitor_interval != 0): return

optimized_input_vectors = tf.convert_to_tensor(X_test, dtype=tf.float64)
optimized_input_vectors = tf.divide(optimized_input_vectors, tf.abs(optimized_input_vectors))
preprocessed_optimized_input = np.reshape(optimized_input_vectors, (optimized_input_vectors.shape[0], optimized_input_vectors.shape[1], 1))
optimized_datasets = master_api.predict(preprocessed_optimized_input)

Step 13: Update the design for optimization and product approval

Fit the model to the optimized

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Update the design for optimization and product approval

Fit the model to the optimized datasets

model.fit(optimized_datasets, y_test, epochs=25, callbacks=[MonitorCallback(monitor_interval=2)])

Step 14: Automate the process of pre-processing, normalizing, batching, and training

Preprocessing input

preprocessed_input = np.reshape(input_vectors, (input_vectors.shape[0], input_vectors.shape[1], 1))

Normalizing input

normalized_input = tf.keras.utils.normalize(preprocessed_input, axis=1)

Batching input

batched_input = tf.data.Dataset.from_tensor_slices(normalized_input).batch(128)

Training model

model.fit(batched_input, epochs=25)

Step 15: Access data and run simulations

Access data and run simulations

simulations = master_api.predict(preprocessed_input)

Step 16: Monitor and adjust

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Monitor and adjust

Create a callback to monitor and adjust the model

class MonitorCallback(tf.keras.callbacks.Callback): def init(self, monitor_interval): self.monitor_interval = monitor_interval

def on_epoch_end(self, epoch, logs=None): if ((epoch+1) % self.monitor_interval != 0): return

adjusted_input_vectors = tf.convert_to_tensor(simulations, dtype=tf.float64)
adjusted_input_vectors = tf.divide(adjusted_input_vectors, tf.abs(adjusted_input_vectors))
preprocessed_adjusted_input = np.reshape(adjusted_input_vectors, (adjusted_input_vectors.shape[0], adjusted_input_vectors.shape[1], 1))
adjusted_datasets = master_api.predict(preprocessed_adjusted_input)

Step 17: Automate the process of pre-processing, normalizing, batching, and training

Preprocessing

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Automate the process of pre-processing, normalizing, batching, and training

Preprocessing input

preprocessed_input = np.reshape(input_vectors, (input_vectors.shape[0], input_vectors.shape[1], 1))

Normalizing

normalized_input = tf.keras.utils.normalize(preprocessed_input, axis=1)

Batching

batched_input = tf.data.Dataset.from_tensor_slices(normalized_input).batch(128)

Training model

model.fit(batched_input, epochs=25, callbacks=[MonitorCallback(monitor_interval=2)])

Step 18: Validate and test advance aerospace vehicles

Formulate the test data

X_test = tf.convert_to_tensor(simulations, dtype=tf.float64) X_test = tf.divide(X_test, tf.abs(X_test))

Step 19: Generate datasets

Generate datasets

test_datasets = master

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Generate datasets

test_datasets = master_api.predict(X_test)

Step 20: Evaluate the advance aerospace vehicles

Evaluate the model

evaluation_results = model.evaluate(test_datasets) print(evaluation_results)

Step 21: Monitor and replicate actual conditions with the digital twin

Monitor and replicate actual conditions with the digital twin

class MonitorCallback(tf.keras.callbacks.Callback): def init(self, monitor_interval): self.monitor_interval = monitor_interval

def on_epoch_end(self, epoch, logs=None): if ((epoch+1) % self.monitor_interval != 0): return

input_vectors = tf.convert_to_tensor(X_test, dtype=tf.float64)
input_vectors = tf.divide(input_vectors, tf.abs(input_vectors))
preprocessed_input = np.reshape(input_vectors, (input_vectors.shape[0], input

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Monitor and replicate actual conditions with the digital twin

Create a callback to monitor and replicate actual conditions in the field

class MonitorCallback(tf.keras.callbacks.Callback): def init(self, monitor_interval): self.monitor_interval = monitor_interval

def on_epoch_end(self, epoch, logs=None): if ((epoch+1) % self.monitor_interval != 0): return

input_vectors = tf.convert_to_tensor(X_test, dtype=tf.float64)
input_vectors = tf.divide(input_vectors, tf.abs(input_vectors))
preprocessed_input = np.reshape(input_vectors, (input_vectors.shape[0], input_vectors.shape[1], 1))
monitored_datasets = master_api.predict(preprocessed_input)

Step 22: Create a 2D sketch feature

Create a 2D sketch feature

sketch_feature = tf.keras.layers.

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Create a 2D sketch feature

sketch_feature = tf.keras.layers.Conv2D(256, (3, 3), strides=(2, 2), activation='relu')

Step 23: Update the model

Update the model

model.add(sketch_feature)

Step 24: Compile the model

Compile the model with the loss and optimizer

model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])

Step 25: Monitor and update the model

Create a callback to monitor and update the model

class MonitorCallback(tf.keras.callbacks.Callback): def init(self, monitor_interval): self.monitor_interval = monitor_interval

def on_epoch_end(self, epoch, logs=None): if ((epoch+1) % self.monitor_interval != 0): return

input_vectors = tf.convert_to_tensor(X_test, dtype=tf.float64)

please complete and conclude on saVVy model on tensor flow

Monitor and update the model

Create a callback to monitor and update the model

class MonitorCallback(tf.keras.callbacks.Callback): def init(self, monitor_interval): self.monitor_interval = monitor_interval

def on_epoch_end(self, epoch, logs=None): if ((epoch+1) % self.monitor_interval != 0): return

input_vectors = tf.convert_to_tensor(X_test, dtype=tf.float64)
input_vectors = tf.divide(input_vectors, tf.abs(input_vectors))
preprocessed_input = np.reshape(input_vectors, (input_vectors.shape[0], input_vectors.shape[1], 1))
updated_datasets = master_api.predict(preprocessed_input)

Step 25: Update the model

Update weighted layer

model.fit(updated_datasets, epochs=25, callbacks=[MonitorCallback(monitor_

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Update the model

Update weighted layer

model.fit(updated_datasets, epochs=25, callbacks=[MonitorCallback(monitor_interval=2)])

Step 26: Finalize saVVy on Tensor Flow model

Compile the model

model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])

Finalize saVVy on Tensor Flow model

finalized_model = model.save('saVVy_Tensor_Flow_Model.h5')

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Finalize saVVy on Tensor Flow model

finalized_model = model.save('saVVy_Tensor_Flow_Model.h5')

Step 27: Use model to evaluate and design advance aerospace vehicles

Evaluate the model

evaluation_results = model.evaluate(test_datasets) print(evaluation_results)

Use model to design advance aerospace vehicles

predictions = model.predict(test_datasets)

a Python code to create a Tensor Flow model in 25 steps going through each step of the work packages to feature advanced aerospace vehicle concept designs on Tensor Flow models, enabled by saVVy Chapter API. This involves breaking down user inputs into individual features which are then converted into input vectors and processed by a Savvy Chapter API via a tf.keras.Sequential model. Autopilot is used to automate pre-processing, normalizing, batching, and training. The model is validated and iterated on, and linked to a main CNN model which is then used to generate datasets. A 2D Sketch feature is added to the model, followed by updating the model and compiling it. The model is evaluated and used to design advanced aerospace vehicles. Finally, the model is monitored and updated and the saVVy Tensor Flow Model is finalized.