+
+ 
+
+
+
+
+ 
+ 
+ + created with +
+ + Website Builder Software + . +
+ + created with +
+ + Website Builder Software + . +
+ 
+ Billions of pounds of trash and other pollutants enter the oceans every year. There are many causes of ocean pollution, which include intentional discharge and littering. Numerous items such as disposable plastic products and styrofoam containers which directly and indirectly affect marine life, as well as humans. +
+Our preliminary solution is divided into two parts:
+
MACRO and MICRO
+
+
Macroscopically
Future movement of microplastics along the coasts would be modelled and predicted with a neural network. Traditional methods like using the Lagrangian or direct simulations have to choose between run-time performance or prediction accuracy.
+
Using neural networks, we would have a better trade-off and the simulation can be run much computationally efficiently. This can be done by applying Proper Orthogonal Decomposition, characterizing only limited degrees of freedom and using flow lines rather than particles for the sake of efficiency.
+
+
Microscopically
+
Due to the difficulties in detecting microplastics chemically, we propose the use of neural networks to detect and classify microplastics in a water sample.
+
+ As we strive to overcome climate change, pollution, and the crisis of exhausting energy and resources, we aim to develop new sustainable approaches so that we and our children's generation can enjoy quality of life on a planet with a clean environment and abundant biodiversity.
+ Learn More
+ The support and participation of individuals, companies and governments around the globe will allow us to achieve our mission and make progress towards a future without plastic in our oceans.
+ Learn More+ created with +
+ + HTML Creator + . +
+ 
+ Macroplastics
+
+ 
+ Microplastics
+Click the icon above or the button below to upload (Currently unavailable) +
+Click the icon above or the button below to upload (Currently unavailable) +
+
Upload File
+
+ Upload File
+
+ + created with +
+ + Website Builder Software + . +
+ 
+ By customizing the neural network with tensors, the network will treat movement after rotation and flipping in the same way. Simulation data generated with will be used to train the neural network. To enhance the details of the flow field and turbulence, we can employ current image super-resolution techniques.
++ PARCELS +
+
+ + created with +
+ + Website Builder Software + . +
+