Enforce EP

calculations/PMM.py

@@ -9,6 +9,9 @@ df.loc[df['re_E'] < 0, 'im_E'] = 0 # set im_E = 0 for bound states (to avoid squ
 df['E'] = df['re_E'] + 1j * df['im_E']
 df['k'] = np.sqrt(df['E'])
 
+c0 = df[df['E'] == 0]['c'].values[0]
+df['c'] = df['c'] - c0 # shift c to set c=0 at the exceptional point
+
 train_data = df[df['re_E'] < 0]
 target_data = df[df['re_E'] > 0]
 
@@ -25,6 +28,14 @@ H0 = (H0 + torch.transpose(H0, 0, 1)).requires_grad_() # symmetric
 H1 = torch.randn(N, N, dtype=torch.complex128)
 H1 = (H1 + torch.transpose(H1, 0, 1)).requires_grad_() # symmetric
 
+def enforce_ep():  # enforce exceptional point at c=0
+    with torch.no_grad():
+        H0[0:2, :] = 0
+        H0[:, 0:2] = 0
+        H0[0, 1] = 1
+
+enforce_ep()
+
 #%%
 # training
 
@@ -60,6 +71,7 @@ for epoch in range(epochs):
     with torch.no_grad():
         H0 -= lr * H0.grad
         H1 -= lr * H1.grad
+    enforce_ep()
 
 # %%
 # evaluate for all points