## Mahdi Naderibeni

4 records found

1

## Authored

## Learning Reduced-Order Mappings between Functions

### An Investigation of Suitable Inputs and Outputs

## Learning Reduced Order Mappings of Navier-Stokes

### An Investigation of Generalization on the Viscosity Parameter

## Data Driven Approximations Of PDEs

### On Robustness of Reduced Order Mappings between Function Spaces Against Noise

equations (PDEs). In PINNs, physical laws are incorporated into the loss function, guiding the network to learn a model that adheres to these laws as defined by the PDEs. Train ...

## Contributed

## Learning Reduced-Order Mappings between Functions

### An Investigation of Suitable Inputs and Outputs

## Learning Reduced-Order Mappings between Functions

### An Investigation of Suitable Inputs and Outputs

## Learning Reduced Order Mappings of Navier-Stokes

### An Investigation of Generalization on the Viscosity Parameter

## Learning Reduced Order Mappings of Navier-Stokes

### An Investigation of Generalization on the Viscosity Parameter

## Data Driven Approximations Of PDEs

### On Robustness of Reduced Order Mappings between Function Spaces Against Noise

## Data Driven Approximations Of PDEs

### On Robustness of Reduced Order Mappings between Function Spaces Against Noise

equations (PDEs). In PINNs, physical laws are incorporated into the loss function, guiding the network to learn a model that adheres to these laws as defined by the PDEs. Train ...

equations (PDEs). In PINNs, physical laws are incorporated into the loss function, guiding the network to learn a model that adheres to these laws as defined by the PDEs. Train ...