update system.show() method

citations to koopman examples

examples/ODEs/Part_7_DeepKoopman.ipynb

@@ -28,12 +28,14 @@
     "[3] [E. Yeung, S. Kundu and N. Hodas, \"Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems,\" 2019 American Control Conference (ACC), Philadelphia, PA, USA, 2019, pp. 4832-4839](https://ieeexplore.ieee.org/document/8815339)  \n",
     "[4] [Shaowu Pan, Karthik Duraisamy, Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability, SIAM Journal on Applied Dynamical Systems, 2020](https://epubs.siam.org/doi/abs/10.1137/19M1267246?journalCode=sjaday)  \n",
     "[5] [F. Fan, B. Yi, D. Rye, G. Shi and I. R. Manchester, \"Learning Stable Koopman Embeddings,\" 2022 American Control Conference (ACC), Atlanta, GA, USA, 2022, pp. 2742-2747](https://ieeexplore.ieee.org/document/9867865)  \n",
+    "[6] https://pubs.aip.org/aip/cha/article-abstract/22/4/047510/341880/Applied-Koopmanisma  \n",
+    "[7] https://arxiv.org/abs/1312.0041  \n",
     "\n",
     "\n",
     "### Generic Stable Layers References\n",
-    "[6]  [E. Skomski, S. Vasisht, C. Wight, A. Tuor, J. Drgoňa and D. Vrabie, \"Constrained Block Nonlinear Neural Dynamical Models,\" 2021 American Control Conference (ACC), New Orleans, LA, USA, 2021, pp. 3993-4000](https://ieeexplore.ieee.org/document/9482930)   \n",
-    "[7] [J. Drgoňa, A. Tuor, S. Vasisht and D. Vrabie, \"Dissipative Deep Neural Dynamical Systems,\" in IEEE Open Journal of Control Systems, 2022](https://ieeexplore.ieee.org/abstract/document/9809789)  \n",
-    "[8] [Jiong Zhang, Qi Lei, Inderjit S. Dhillon, Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization, InternationalConferenceonMachine Learning, 2018](https://arxiv.org/abs/1803.09327)\n",
+    "[8]  [E. Skomski, S. Vasisht, C. Wight, A. Tuor, J. Drgoňa and D. Vrabie, \"Constrained Block Nonlinear Neural Dynamical Models,\" 2021 American Control Conference (ACC), New Orleans, LA, USA, 2021, pp. 3993-4000](https://ieeexplore.ieee.org/document/9482930)   \n",
+    "[9] [J. Drgoňa, A. Tuor, S. Vasisht and D. Vrabie, \"Dissipative Deep Neural Dynamical Systems,\" in IEEE Open Journal of Control Systems, 2022](https://ieeexplore.ieee.org/abstract/document/9809789)  \n",
+    "[10] [Jiong Zhang, Qi Lei, Inderjit S. Dhillon, Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization, InternationalConferenceonMachine Learning, 2018](https://arxiv.org/abs/1803.09327)\n",
     "\n"
    ]
   },
@@ -321,7 +323,7 @@
     "$$\\ell_{V} = || I - VV^T||_2 + || I - V^TV||_2  $$\n",
     "$$\\ell_{\\text{stable}} = \\ell_{U} + \\ell_{V} $$\n",
     "\n",
-    "For more details on the SVD and other linear algebra factorizations of trainable linear layers see the references [[6]](https://ieeexplore.ieee.org/document/9482930) and [[7]](https://ieeexplore.ieee.org/abstract/document/9809789), with Pytorch implementations in the [slim submodule](https://github.com/pnnl/neuromancer/tree/master/src/neuromancer/slim) of the Neuromancer library. "
+    "For more details on the SVD and other linear algebra factorizations of trainable linear layers see the references [[8]](https://ieeexplore.ieee.org/document/9482930) and [[9]](https://ieeexplore.ieee.org/abstract/document/9809789), with Pytorch implementations in the [slim submodule](https://github.com/pnnl/neuromancer/tree/master/src/neuromancer/slim) of the Neuromancer library. "
    ]
   },
   {

examples/ODEs/Part_7_DeepKoopman.py

@@ -6,9 +6,11 @@
 [2] https://ieeexplore.ieee.org/document/8815339
 [3] https://arxiv.org/abs/1710.04340
 [4] https://nicholasgeneva.com/deep-learning/koopman/dynamics/2020/05/30/intro-to-koopman.html
+[5] https://pubs.aip.org/aip/cha/article-abstract/22/4/047510/341880/Applied-Koopmanisma
+[6] https://arxiv.org/abs/1312.0041
 
 references stability:
-[5] https://ieeexplore.ieee.org/document/9482930
+[7] https://ieeexplore.ieee.org/document/9482930
 
 """
 

examples/ODEs/Part_8_nonauto_DeepKoopman.py

@@ -7,9 +7,11 @@
 [3] https://ieeexplore.ieee.org/document/9799788
 [4] https://ieeexplore.ieee.org/document/9022864
 [5] https://github.com/HaojieSHI98/DeepKoopmanWithControl
+[6] https://pubs.aip.org/aip/cha/article-abstract/22/4/047510/341880/Applied-Koopmanisma
+[7] https://arxiv.org/abs/1312.0041
 
 references stability:
-[6] https://ieeexplore.ieee.org/document/9482930
+[8] https://ieeexplore.ieee.org/document/9482930
 
 """
 

examples/control/Part_4_NODE_control.py

@@ -136,6 +136,9 @@ def forward(self, x, R):
         features = torch.cat([x, R], dim=-1)
         return self.net(features)
 
+# fix model parameters
+system_nodel.requires_grad_(False)
+
 insize = 2*nx
 policy = Policy(insize, nu)
 policy_node = Node(policy, ['xn', 'R'], ['U'], name='policy')

examples/control/Part_6_DeepKoopman_DPC.py

@@ -177,7 +177,7 @@ def forward(self, x, u):
                     nonlin=torch.nn.ELU,
                     hsizes=n_layers*[n_hidden])
     # predicted trajectory decoder
-    decode_y = Node(f_y_inv, ['x'], ['yhat'], name='decoder_y')
+    decode_y = Node(f_y_inv, ['x'], ['y'], name='decoder_y')
 
     # instantiate SVD factorized Koopman operator with bounded eigenvalues
     K = slim.linear.SVDLinear(nx_koopman, nx_koopman,
@@ -195,10 +195,11 @@ def forward(self, x, u):
 
     # latent Koopmann rollout
     dynamics_model = System([Koopman], name='Koopman', nsteps=nsteps)
+    dynamics_model.show()
 
     # variables
     Y = variable("Y")  # observed
-    yhat = variable('yhat')  # predicted output
+    yhat = variable('y')  # predicted output
     x_latent = variable('x_latent')  # encoded output trajectory in the latent space
     u_latent = variable('u_latent')  # encoded input trajectory in the latent space
     x = variable('x')  # Koopman latent space trajectory
@@ -254,7 +255,7 @@ def forward(self, x, u):
     problem.nodes[3].nsteps = test_data['Y'].shape[1]
     test_outputs = problem.step(test_data)
 
-    pred_traj = test_outputs['yhat'][:, 1:-1, :].detach().numpy().reshape(-1, nx).T
+    pred_traj = test_outputs['y'][:, 1:-1, :].detach().numpy().reshape(-1, nx).T
     true_traj = test_data['Y'][:, 1:, :].detach().numpy().reshape(-1, nx).T
     input_traj = test_data['U'].detach().numpy().reshape(-1, nu).T
 
@@ -302,15 +303,15 @@ def forward(self, x, u):
     ref = torch.cat(list_refs)
     batched_ref = ref.reshape([n_samples, nsteps+1, nref])
     # Training dataset
-    train_data = DictDataset({'x': torch.rand(n_samples, 1, nx),
+    train_data = DictDataset({'y': torch.rand(n_samples, 1, ny),
                               'r': batched_ref}, name='train')
 
     # references for dev set
     list_refs = [torch.rand(1, 1)*torch.ones(nsteps+1, nref) for k in range(n_samples)]
     ref = torch.cat(list_refs)
     batched_ref = ref.reshape([n_samples, nsteps+1, nref])
     # Development dataset
-    dev_data = DictDataset({'x': torch.rand(n_samples, 1, nx),
+    dev_data = DictDataset({'y': torch.rand(n_samples, 1, ny),
                             'r': batched_ref}, name='dev')
 
     # torch dataloaders
@@ -322,18 +323,25 @@ def forward(self, x, u):
                                              collate_fn=dev_data.collate_fn,
                                              shuffle=False)
 
+    """
+    # # #  Deep Koopman DPC architecture
+    """
+    # state encoder
+    encode_y = Node(f_y, ['y'], ['x'], name='encoder_y')
 
-
-    # initial condition encoder
-    encode_y = Node(f_y, ['y'], ['x'], name='encoder_Y0')
+    # fix parameters of the Koopman model
+    encode_y.requires_grad_(False)
+    encode_U.requires_grad_(False)
+    dynamics_model.requires_grad_(False)
+    decode_y.requires_grad_(False)
 
     # neural net control policy
     net = blocks.MLP(insize=nx + nref, outsize=nu, hsizes=4*[32],
                             nonlin=torch.nn.ELU)
-    policy_node = Node(net, ['y', 'R'], ['U'], name='policy')
+    policy = Node(net, ['y', 'r'], ['U'], name='policy')
 
-    nodes = [encode_y, policy_node, encode_U, dynamics_model, decode_y]
-    cl_system = System(nodes, name='cl_system')
+    nodes = [encode_y, policy, encode_U, dynamics_model, decode_y]
+    cl_system = System(nodes, name='cl_system', nsteps=nsteps)
     cl_system.show()
 
     """

src/neuromancer/psl/nonautonomous.py

@@ -255,6 +255,7 @@ def equations(self, t, x, u):
             * Caf = Feed Concentration (mol/m^3)
         States (2):
             * Concentration of A in CSTR (mol/m^3)
+            * Temperature in CSTR (K)
         """
         Tc = u  # Temperature of cooling jacket (K)
         Ca = x[0]  # Concentration of A in CSTR (mol/m^3)

src/neuromancer/system.py

@@ -150,25 +150,22 @@ def graph(self):
                         graph.add_edge(pydot.Edge(src.name, dst.name, label=key))
                         unique_common_keys.add(key)
 
-        # get keys of recurrent nodes
+        # build I/O and node loop connections
         loop_keys = []
-        for node in self.nodes:
+        init_keys = []
+        previous_output_keys = []
+        for idx_node, node in enumerate(self.nodes):
             node_loop_keys = set(node.input_keys) & set(node.output_keys)
             loop_keys += node_loop_keys
-        # get keys required as input and to initialize some nodes
-        init_keys = set(input_keys) - (set(output_keys) - set(loop_keys))
+            init_keys += set(node.input_keys) - set(previous_output_keys)
+            previous_output_keys += node.output_keys
 
-        # build I/O and node loop connections
-        previous_output_keys = []
-        for idx_node, node in enumerate(self.nodes):
             # build single node recurrent connections
-            node_loop_keys = list(set(node.input_keys) & set(node.output_keys))
             for key in node_loop_keys:
                 graph.add_edge(pydot.Edge(node.name, node.name, label=key))
             # build connections to the dataset
-            for key in set(node.input_keys) & set(init_keys-set(previous_output_keys)):
+            for key in set(node.input_keys) & set(init_keys):
                 graph.add_edge(pydot.Edge("in", node.name, label=key))
-            previous_output_keys += node.output_keys
             # build feedback connections for init nodes
             feedback_src_nodes = reverse_order_nodes[:-1-idx_node]
             if len(set(node.input_keys) & set(loop_keys) & set(init_keys)) > 0:
@@ -177,6 +174,7 @@ def graph(self):
                         if key in src.output_keys and key not in previous_output_keys:
                             graph.add_edge(pydot.Edge(src.name, node.name, label=key))
                             break
+
         # build connections to the output of the system in a reversed order
         previous_output_keys = []
         for node in self.nodes[::-1]:
@@ -189,7 +187,7 @@ def graph(self):
         return graph
 
     def show(self, figname=None):
-        graph = self.system_graph
+        graph = self.graph()
         if figname is not None:
             plot_func = {'svg': graph.write_svg,
                          'png': graph.write_png,